Patent Publication Number: US-11393044-B2

Title: Systems and methods for associating related merchants

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 14/054,340 filed on Oct. 15, 2013, entitled “SYSTEMS AND METHODS FOR ASSOCIATING RELATED MERCHANTS”, which is hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND OF THE DISCLOSURE 
     This disclosure relates generally to aggregating merchant transaction information and, more particularly, to a system and method for associating related merchants with a parent “aggregate merchant” to facilitate the aggregation of transaction data across related merchants. 
     A processor of financial transactions, such as payment transactions, processes transactions between many types of entities, such as personal consumers (e.g., individuals, or “cardholders”) and merchants (e.g., the businesses selling those goods and services). Some merchants, such as a retail outlet store or a restaurant, process transactions involving their consumers using a point-of-sale (“POS”) device connected to a payment network. During a typical payment card transaction, the cardholder identifies his/her payment account, such as a credit card account, by swiping their payment card through the POS device. The merchant is identified through the use of pre-configured merchant information stored within the POS device. Thus, when the POS device interacts with the payment network to perform the transaction, both the consumer data from the payment card and the merchant data from the POS device are sent through to the payment network for processing. 
     Payment networks receive transactions involving many types of merchants. Some businesses are conventional small-business operators, such as a single-store restaurant (i.e., a single merchant at a single location). Other businesses are franchise businesses with many franchisees, each of which may operate one or more storefronts (i.e., multiple merchants/locations). Still other businesses are corporate businesses which may operate many storefronts themselves (i.e., multiple merchants/locations). A business with multiple merchants may be thought of, and analyzed as, an aggregate merchant. It would be beneficial to be able to aggregate multiple related merchants to a single aggregate merchant. 
     BRIEF DESCRIPTION OF THE DISCLOSURE 
     In one aspect, a computer-implemented method for associating a merchant with an aggregate merchant is provided. The method uses a computing device having a processor and a memory. The method includes identifying an association rule for the aggregate merchant. The association rule includes one or more antecedents. Each antecedent includes a model value for the antecedent associated with the aggregate merchant. The method also includes identifying one or more merchant data values associated with the merchant. Each of the one or more merchant data values correspond to one of the one or more antecedents. The method further includes applying, by the computing device, the association rule to the one or more merchant data values by comparing the model value for each antecedent with a merchant data value associated with the corresponding antecedent, thereby generating a confidence score for the merchant. The confidence score represents a likelihood the merchant is associated with the aggregate merchant. The method also includes outputting the confidence score. 
     In another aspect, a computing device for associating a merchant with an aggregate merchant is provided. The computer device includes a processor communicatively coupled to a memory. The computing device is programmed to identify, within the memory, an association rule for the aggregate merchant. The association rule includes one or more antecedents. Each antecedent includes a model value for the antecedent associated with the aggregate merchant. The computing device is also programmed to identify, within the memory, one or more merchant data values associated with the merchant. Each of the one or more merchant data values corresponding to one of the one or more antecedents. The computing device is further programmed to apply the association rule to the one or more merchant data values by comparing the model value for each antecedent with a merchant data value associated with the corresponding antecedent, thereby generating a confidence score for the merchant. The confidence score represents a likelihood the merchant is associated with the aggregate merchant. The computing device is also programmed to output the confidence score. 
     In yet another aspect, at least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon is provided. When executed by at least one processor, the computer-executable instructions cause the processor to identify, within the memory, an association rule for an aggregate merchant. The association rule includes one or more antecedents. Each antecedent includes a model value for the antecedent associated with the aggregate merchant. The computer-executable instructions also cause the processor to identify, within the memory, one or more merchant data values associated with a merchant. Each of the one or more merchant data values correspond to one of the one or more antecedents. The computer-executable instructions further cause the processor to apply the association rule to the one or more merchant data values by comparing the model value for each antecedent with a merchant data value associated with the corresponding antecedent, thereby generating a confidence score for the merchant. The confidence score representing a likelihood the merchant is associated with the aggregate merchant. The computer-executable instructions also cause the processor to output the confidence score. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1-11  show example embodiments of the methods and systems described herein. 
         FIG. 1  is a flowchart illustrating a conventional transaction process involving ordinary payment-by-card transactions. 
         FIG. 2  is a simplified block diagram of an example aggregation computing system including a plurality of computer devices connected in communication in accordance with the present disclosure. 
         FIG. 3  is an expanded block diagram of an example aggregation computing system having a server architecture and computer devices in accordance with one embodiment of the present disclosure. 
         FIG. 4  illustrates an example configuration of a user system operated by a user, such as the client devices shown in  FIGS. 2 and 3 . 
         FIG. 5  illustrates an example configuration of a server system such as server systems shown in  FIGS. 2 and 3 . 
         FIG. 6  illustrates example sets of data used by the aggregation system shown in  FIG. 2  to manage associations of merchants to aggregate merchants. 
         FIG. 7  illustrates example data sets used by the aggregation system shown in  FIG. 1  during merchant association analysis. 
         FIG. 8  is an example method for associating related merchants implemented using the aggregation computing system shown in  FIGS. 2 and 3 . 
         FIG. 9  illustrates additional steps for associating related merchants that, in some embodiments, are used for applying the association rule to the merchant data value as shown in  FIG. 8 . 
         FIG. 10  is an example method for building rules and sets of rules that may be used to associate related merchants in accordance with the present disclosure. 
         FIG. 11  shows an example configuration of a database in communication with the aggregation computing systems shown in  FIGS. 2 and 3 . 
     
    
    
     DETAILED DESCRIPTION OF THE DISCLOSURE 
     The methods and systems described herein include systems and methods for associating individual merchants, such as an individual business store or location, with a related “aggregate merchant,” such as a parent company of that individual store/location. The methods and systems described herein facilitate discovering and maintaining these individual merchant to aggregate merchant relationships so that, for example, transaction sales in a payment network may be computed for an aggregate merchant rather than for single individual stores. 
     In one embodiment, an administrator or user of the system builds an “association rule” for a particular aggregate merchant. The rule includes several elements of data, or “antecedents”, that help identify the aggregate merchant, such as a business name, or an acquiring ID (a code used internally by an acquirer for merchant identification) of the aggregate merchant. Merchant data is collected for various merchants and compared to the data in the rule. This merchant data may be generated from merchant transaction data, such as when consumers purchase goods or services from a Merchant A, who then transmits transaction data to a payment network for authorization and other processing. The system applies the rule to the merchant data for Merchant A, comparing a model value for the antecedent (from the rule) with a corresponding value for Merchant A. A score is generated that represents an approximation of how likely it is that Merchant A is associated with the aggregate merchant. 
     For example, presume an aggregate merchant “Wally&#39;s Trout Shop” has several store locations, each of which has a point-of-sale (“POS”) device used to process consumer payment card transactions with a payment network. Most of Wally&#39;s POS devices are configured to identify their transactions under a merchant Doing Business As (“DBA”) name of “WALLYSTROUTSHOP”. However, each POS device may have slightly different information configured within it, and they may not be properly configured with identical names. Presume Wally opens a new store location, and the system then receives a transaction from the new store/location, but the new POS device at the new location identifies itself with a merchant DBA name of “WALLYSFISHSHOP”. This system may not yet know that this individual merchant (i.e., the new POS device at the new store/location identifying itself as “WALLYSFISHSHOP”) is actually associated with the aggregate merchant known as “Wally&#39;s Trout Shop” (i.e., the location known as “WALLYSFISHSHOP” is what is sometimes called an “orphaned merchant”). As such, when other computations within the system wish to examine transactions for all of Wally&#39;s locations, the system will exclude the new location because it is not yet associated with the aggregate merchant. 
     An administrator may create a rule that helps identify merchants that are dissociated with a particular aggregate merchant. The rule may include, for example, a merchant DBA name of “WALLYSTROUTSHOP”, which is configured as the “model data” for Wally&#39;s store locations. The system compares the merchant DBA name of the rule (i.e., “WALLYSTROUTSHOP”) with the merchant data from the individual store (i.e., “WALLYSFISHSHOP”) to evaluate potential association. The system may also compare the rule with other merchants, such as another (properly unrelated) merchant that is using “FISHEMPORIUM” as their DBA name. The system generates comparison values for each of these merchants based on the rule. The merchants with information more similar to the rule are scored higher than merchants that are less similar to the rule. An auditor, for example, may then examine the resulting scores for each merchant and use that data to decide which merchants are actually associated with the Wally&#39;s Fish Shop. 
     A technical effect of the systems and processes described herein include at least one of (a) identifying an association rule for an aggregate merchant that includes one or more antecedents, where each antecedent includes a model value for the antecedent associated with the aggregate merchant; (b) identifying one or more merchant data values associated with a merchant, the one or more merchant data values each corresponding to an antecedent of the one or more antecedents; (c) applying the association rule to the one or more merchant data values by comparing the model value for each antecedent with a merchant data value associated with the corresponding antecedent, thereby generating a confidence score for the merchant, the confidence score representing a likelihood of association between the merchant and the aggregate merchant; and (d) outputting the confidence score. 
     In addition, the systems and processes described herein may include (e) applying the association rule to a plurality of merchants, thereby generating a plurality of confidence scores, wherein applying the association rule includes generating a plurality of confidence sub-values, each confidence sub-value corresponding to an antecedent of the one or more antecedents and combining the plurality of confidence sub-values to generate the confidence score. Identifying an association rule may include identifying the association rule including one or more antecedents, each antecedent further including an associated weight, weighing each sub-value of the plurality of sub-values with the associated weight prior to combining, and/or identifying an association rule that includes one or more antecedents, each antecedent having an associated comparison type. Further, applying the association rule may include comparing based at least in part on the comparison type and/or generating n-gram representations for the model value of an antecedent and a corresponding merchant data value. 
     As used herein, the term “database” may refer to either a body of data, a relational database management system (RDBMS), or to both. As used herein, a database may include any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, and any other structured collection of records or data that is stored in a computer system. The above examples are example only, and thus are not intended to limit in any way the definition and/or meaning of the term database. Examples of RDBMS&#39;s include, but are not limited to including, Oracle® Database, MySQL, IBM® DB2, Microsoft® SQL Server, Sybase®, and PostgreSQL. However, any database may be used that enables the systems and methods described herein. (Oracle is a registered trademark of Oracle Corporation, Redwood Shores, Calif.; IBM is a registered trademark of International Business Machines Corporation, Armonk, N.Y.; Microsoft is a registered trademark of Microsoft Corporation, Redmond, Wash.; and Sybase is a registered trademark of Sybase, Dublin, Calif.) 
     As used herein, a processor may include any programmable system including systems using micro-controllers, reduced instruction set circuits (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuit or processor capable of executing the functions described herein. The above examples are example only, and are thus not intended to limit in any way the definition and/or meaning of the term “processor.” 
     As used herein, the terms “software” and “firmware” are interchangeable, and include any computer program stored in memory for execution by a processor, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are example only, and are thus not limiting as to the types of memory usable for storage of a computer program. 
     In one embodiment, a computer program is provided, and the program is embodied on a computer readable medium. In an example embodiment, the system is executed on a single computer system, without requiring a connection to a sever computer. In a further embodiment, the system is being run in a Windows® environment (Windows is a registered trademark of Microsoft Corporation, Redmond, Wash.). In yet another embodiment, the system is run on a mainframe environment and a UNIX® server environment (UNIX is a registered trademark of X/Open Company Limited located in Reading, Berkshire, United Kingdom). The application is flexible and designed to run in various different environments without compromising any major functionality. In some embodiments, the system includes multiple components distributed among a plurality of computing devices. One or more components may be in the form of computer-executable instructions embodied in a computer-readable medium. The systems and processes are not limited to the specific embodiments described herein. In addition, components of each system and each process can be practiced independent and separate from other components and processes described herein. Each component and process can also be used in combination with other assembly packages and processes. 
     As used herein, the terms “transaction card,” “financial transaction card,” and “payment card” refer to any suitable transaction card, such as a credit card, a debit card, a prepaid card, a charge card, a membership card, a promotional card, a frequent flyer card, an identification card, a prepaid card, a gift card, and/or any other device that may hold payment account information, such as mobile phones, Smartphones, personal digital assistants (PDAs), key fobs, and/or computers. Each type of transactions card can be used as a method of payment for performing a transaction. In addition, consumer card account behavior can include but is not limited to purchases, management activities (e.g., balance checking), bill payments, achievement of targets (meeting account balance goals, paying bills on time), and/or product registrations (e.g., mobile application downloads). 
     The systems and processes are not limited to the specific embodiments described herein. In addition, components of each system and each process can be practiced independent and separate from other components and processes described herein. Each component and process also can be used in combination with other assembly packages and processes. 
     The following detailed description illustrates embodiments of the present disclosure by way of example and not by way of limitation. It is contemplated that the disclosure has general application to transaction analysis. 
     As used herein, an element or step recited in the singular and proceeded with the word “a” or “an” should be understood as not excluding plural elements or steps, unless such exclusion is explicitly recited. Furthermore, references to “example embodiment” or “one embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. 
       FIG. 1  is a flowchart  120  illustrating a conventional transaction process involving ordinary payment-by-card transactions. Embodiments described herein may relate to a payment network  136 , such as a payment card payment system using the MasterCard® interchange network. The MasterCard® interchange network is a set of proprietary communications standards promulgated by MasterCard International Incorporated® for the exchange of financial transaction data and the settlement of funds between financial institutions that are members of MasterCard International Incorporated®. (MasterCard is a registered trademark of MasterCard International Incorporated located in Purchase, N.Y.). In some embodiments, payment network  136  is an interchange network. 
     In a typical payment network  136 , financial institutions (i.e., “issuers”)  122  issues transaction cards, such as credit cards and/or debit cards, to consumers or cardholders  124 . Cardholders  124  use the transaction cards to tender payment for purchases  126  of goods and services from merchants, such as an individual merchant  128  (e.g., an independently owned restaurant) or a merchant  132  (e.g., a particular McDonalds® store) that is a part of a parent business entity or “aggregate merchant”  130  (e.g., McDonalds Corporation®) (McDonalds Corporation, Wilmington, Del.). 
     When cardholders  124  tender payment for purchases  126  with a transaction card, merchants  128  and  132  request authorization from a merchant bank or “acquirer”  134  for the amount of the purchase. The request may be performed over the telephone, but is usually performed through the use of a point-of-sale terminal (not shown in  FIG. 1 ), which reads cardholder&#39;s  124  account information from a magnetic stripe, a chip, or embossed characters on the transaction card and communicates electronically with the transaction processing computers of merchant bank  134 . Alternatively, merchant bank  134  may authorize a third party to perform transaction processing on its behalf. In this case, the point-of-sale terminal will be configured to communicate with the third party. Such a third party is usually called a “merchant processor,” an “acquiring processor,” or a “third party processor.” 
     Using payment network  136  (also sometimes referred to as an interchange network), computers of merchant banks  134  or merchant processors will communicate with computers of issuers  122  to determine whether cardholder&#39;s  124  accounts are in good standing and whether the purchase is covered by cardholder&#39;s  124  available credit line. Based on these determinations, the request for authorization will be declined or accepted. If the request is accepted, an authorization code is issued to merchant  128  and  132 . 
     When a request for authorization is accepted, the available credit line of cardholder&#39;s  124  account is decreased. Normally, a charge for a payment card transaction is not posted immediately to cardholder&#39;s  124  account because bankcard associations, such as MasterCard International Incorporated®, have promulgated rules that do not allow merchants  128  and  132  to charge, or “capture,” a transaction until goods are shipped or services are delivered. However, with respect to at least some debit card transactions, a charge may be posted at the time of the transaction. When merchants  128  and  132  ship or deliver the goods or services, merchants  128  and  132  capture the transaction by, for example, appropriate data entry procedures on the point-of-sale terminal. This may include bundling of approved transactions daily for standard retail purchases. If cardholder  124  cancels a transaction before it is captured, a “void” is generated. If cardholders  124  return goods after the transaction has been captured, a “credit” is generated. Payment network  136  and/or issuer banks  122  store the transaction card information, such as a type of merchant, amount of purchase, date of purchase, in a database (not shown in  FIG. 1 ). 
     After a purchase has been made, a clearing process occurs to transfer additional transaction data related to the purchase among the parties to the transaction, such as merchant banks  134 , payment network  136 , and issuer banks  122 . More specifically, during and/or after the clearing process, additional data, such as a time of purchase, a merchant name, a type of merchant, purchase information, cardholder account information, a type of transaction, itinerary information, information regarding the purchased item and/or service, and/or other suitable information, is associated with a transaction and transmitted between parties to the transaction as transaction data, and may be stored by any of the parties to the transaction. 
     After a transaction is authorized and cleared, the transaction is settled among merchant  128  and  132 , merchant bank  134 , and issuer bank  122 . Settlement refers to the transfer of financial data or funds among merchant&#39;s  128  and  132  accounts, merchant bank  134 , and issuer bank  122  related to the transaction. Usually, transactions are captured and accumulated into a “batch,” which is settled as a group. More specifically, a transaction is typically settled between issuer banks  122  and payment network  136 , and then between payment network  136  and merchant banks  134 , and then between merchant banks  134  and merchants  128  and  132 . 
     Further, in the example embodiment, payment network  136  aggregates transaction data to generate, for example, aggregate summary reports  140  for aggregate merchants  130 . To facilitate aggregation of transaction data, payment network  136  maintains relationship information for each merchant  128  and  132 . Some information is gathered from the transaction data transmitted from the merchant  128  and  132  POS devices. For example, in some embodiments, a POS device is configured with an “Acquiring ID” during installation and configuration. The acquiring ID is an identifier, such as a unique integer, that assists in uniquely identifying a particular POS device as against other POS devices of an acquirer. This acquiring ID is transmitted along with other transaction data for every transaction that is processed. As such, each transaction may be associated with a particular merchant  128  and  132 , but each merchant  128  and  132  may or may not be associated with an aggregate merchant  130 . 
     To associate transactions with aggregate merchants  130 , in some embodiments, each merchant  132  that has an affiliation with a parent entity is associated with the appropriate aggregate merchant  130 . Such associations may be stored in a database such that interchange network  136  maintains the aggregation relationships over time. Thus, in this example embodiment, transactions are associated with merchants  128  and  132 , and any transactions of merchants  132  are associated with aggregate merchants  130 , allowing aggregation of transactions at the level of aggregate merchants  130 . Some individual merchants  128  do not have a relationship with an aggregate merchant  130 , and are thus no aggregation association is established and maintained. As used herein, the term “aggregate merchant” is used to refer generally and broadly to some categorization or association of merchants and, in specific embodiments, to either the parent business entity, or the data structure within a computing system (not shown in  FIG. 1 ) that represents the parent business entity, or to both. Similarly, the term “merchant” is used to refer to either a single business location, or to the data structure within the computing system that represents the business location, or to both. 
     In some known systems, the establishing and maintaining of merchant  132  to aggregate merchant  130  relationships requires human involvement to correlate the relationships. In the example embodiments discussed in detail below, the systems and methods presented herein are configured to automate certain management tasks for establishing and maintaining these merchant  132  to aggregate merchant  130  relationships. 
       FIG. 2  is a simplified block diagram of an example aggregation system  200  including a plurality of computing devices connected in communication in accordance with the present disclosure. In the example embodiment, aggregation system  200  may be used for analyzing and maintaining merchant aggregation information, as well as aggregating data based on the merchant aggregation information. More specifically, in the example embodiment, aggregation system  200  includes an aggregation computing device  212 , and a plurality of client sub-systems, also referred to as client systems  214 , connected to aggregation computing device  212 . In one embodiment, client systems  214  are computers associated with one or more of merchants  128  and  132  (shown in  FIG. 1 ), merchant acquirers  134  (shown in  FIG. 1 ), issuers  122  (shown in  FIG. 1 ), and/or cardholders  124  (shown in  FIG. 1 ). Client systems  214  may interconnected through many interfaces including a network  215 , such as a local area network (LAN), a wide area network (WAN), the Internet, dial-in-connections, cable modems, special high-speed Integrated Services Digital Network (ISDN) lines, and RDT networks. Client systems  214  could be any device capable of interconnecting to the network  215  including a web-based phone, PDA, or other web-based connectable equipment. 
     Aggregation system  200  also includes point-of-sale (POS) terminals  218 , which may be connected to client systems  214  and may be connected to aggregation computing device  212 . POS terminals  218  are interconnected to network  215  through many interfaces including a network, such as a local area network (LAN) or a wide area network (WAN), dial-in-connections, cable modems, wireless modems, and special high-speed ISDN lines. POS terminals  218  could be any device capable of interconnecting to network  215  and including an input device capable of reading information from a consumer&#39;s financial transaction card. 
     A database server  216  is connected to database  220 , which contains information on a variety of matters, as described below in greater detail. In one embodiment, centralized database  220  is stored on aggregation computing device  212  and can be accessed by potential users at one of client systems  214  by logging onto aggregation computing device  212  through one of client systems  214 . In an alternative embodiment, database  220  is stored remotely from aggregation computing device  212  and may be non-centralized. 
     Database  220  may include a single database having separated sections or partitions or may include multiple databases, each being separate from each other. Database  220  may store transaction data generated as part of sales activities conducted over the processing network including data relating to merchants, account holders or customers, issuers, acquirers, and/or purchases made. Database  220  may also store account data including at least one of a cardholder name, a cardholder address, an account number, and other account identifier. Database  220  may also store merchant data including a merchant identifier that identifies each merchant registered to use the network, and instructions for settling transactions including merchant bank account information. Database  220  may also store purchase data associated with items being purchased by a cardholder from a merchant, and authorization request data. Database  220  may also store merchant association information for aggregate merchants  130  (shown in  FIG. 1 ). 
     In the example embodiment, one of client systems  214  may be associated with acquirer bank  134  while another one of client systems  214  may be associated with issuer  122 . POS terminal  218  may be associated with a participating merchant  128  and  132  or may be a computer system and/or mobile system used by a cardholder making an on-line purchase or payment. In the example embodiment, aggregation computing device  212  is associated with a network interchange, such as payment network  136  (shown in  FIG. 1 ), and may be referred to as an interchange computer system. Aggregation computing device  212  may be used for processing transaction data. In addition, client systems  214  and/or POS terminal  218  may include a computer system associated with at least one of an online bank, a bill payment outsourcer, an acquirer bank, an acquirer processor, an issuer bank associated with a transaction card, an issuer processor, a remote payment system, and/or a biller. 
       FIG. 3  is an expanded block diagram of an example aggregation computing system  322  having a server architecture and computer devices in accordance with one embodiment of the present disclosure. Components in system  322 , identical to components of aggregation system  200  (shown in  FIG. 2 ), are identified in  FIG. 3  using the same reference numerals as used in  FIG. 2 . System  322  includes aggregation computing device  212 , client systems  214 , and POS terminals  218 . Aggregation computing device  212  further includes database server  216 , a transaction server  324 , a web server  326 , a fax server  328 , a directory server  330 , and a mail server  332 . A storage device  334  is coupled to database server  216  and directory server  330 . Servers  216 ,  324 ,  326 ,  328 ,  330 , and  332  are coupled in a local area network (LAN)  336 . In addition, an issuer bank workstation  338 , an acquirer bank workstation  340 , and a third party processor workstation  342  may be coupled to LAN  336 . In the example embodiment, issuer bank workstation  338 , acquirer bank workstation  340 , and third party processor workstation  342  are coupled to LAN  336  using network connection  215 . Workstations  338 ,  340 , and  342  are coupled to LAN  336  using an Internet link or are connected through an Intranet. 
     Each workstation  338 ,  340 , and  342  is a personal computer having a web browser. Although the functions performed at the workstations typically are illustrated as being performed at respective workstations  338 ,  340 , and  342 , such functions can be performed at one of many personal computers coupled to LAN  336 . Workstations  338 ,  340 , and  342  are illustrated as being associated with separate functions only to facilitate an understanding of the different types of functions that can be performed by individuals having access to LAN  336 . 
     Aggregation computing device  212  is configured to be communicatively coupled to various individuals, including employees  344  and to third parties, e.g., account holders, customers, auditors, developers, consumers, merchants, acquirers, issuers, etc.,  346  using an ISP Internet connection  348 . The communication in the example embodiment is illustrated as being performed using the Internet, however, any other wide area network (WAN) type communication can be utilized in other embodiments, i.e., the systems and processes are not limited to being practiced using the Internet. In addition, and rather than WAN  350 , local area network  215  could be used in place of WAN  350 . 
     In the example embodiment, any authorized individual having a workstation  354  can access system  322 . At least one of the client systems includes a manager workstation  356  located at a remote location. Workstations  354  and  356  are personal computers having a web browser. Also, workstations  354  and  356  are configured to communicate with aggregation computing device  212 . Furthermore, fax server  328  communicates with remotely located client systems, including a client system  356  using a telephone link. Fax server  328  is configured to communicate with other client systems  338 ,  340 , and  342  as well. 
       FIG. 4  illustrates an example configuration of a user system  402  operated by a user  401 , such as the client devices shown in  FIGS. 2 and 3 . User system  402  may include, but is not limited to, client systems  314 ,  338 ,  340 , and  342 , POS terminal  218 , workstation  354 , and manager workstation  356 . In the example embodiment, user system  402  includes a processor  405  for executing instructions. In some embodiments, executable instructions are stored in a memory area  410 . Processor  405  may include one or more processing units, for example, a multi-core configuration. Memory area  410  is any device allowing information such as executable instructions and/or written works to be stored and retrieved. Memory area  410  may include one or more computer readable media. 
     User system  402  also includes at least one media output component  415  for presenting information to user  401 . Media output component  415  is any component capable of conveying information to user  401 . In some embodiments, media output component  415  includes an output adapter such as a video adapter and/or an audio adapter. An output adapter is operatively coupled to processor  405  and operatively couplable to an output device such as a display device, a liquid crystal display (LCD), organic light emitting diode (OLED) display, or “electronic ink” display, or an audio output device, a speaker or headphones. 
     In some embodiments, user system  402  includes an input device  420  for receiving input from user  401 . Input device  420  may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel, a touch pad, a touch screen, a gyroscope, an accelerometer, a position detector, or an audio input device. A single component such as a touch screen may function as both an output device of media output component  415  and input device  420 . User system  402  may also include a communication interface  425 , which is communicatively couplable to a remote device such as aggregation computing device  212 . Communication interface  425  may include, for example, a wired or wireless network adapter or a wireless data transceiver for use with a mobile phone network, Global System for Mobile communications (GSM), 3G, or other mobile data network or Worldwide Interoperability for Microwave Access (WIMAX). 
     Stored in memory area  410  are, for example, computer readable instructions for providing a user interface to user  401  via media output component  415  and, optionally, receiving and processing input from input device  420 . A user interface may include, among other possibilities, a web browser and client application. Web browsers enable users, such as user  401 , to display and interact with media and other information typically embedded on a web page or a website from aggregation computing device  212 . A client application allows user  401  to interact with a server application from aggregation computing device  212 . 
       FIG. 5  illustrates an example configuration of a server system  501  such as aggregation computing device  212  (shown in  FIGS. 2 and 3 ). Server system  501  may include, but is not limited to, database server  216 , transaction server  324 , web server  326 , fax server  328 , directory server  330 , and mail server  332 . 
     Server system  501  includes a processor  505  for executing instructions. Instructions may be stored in a memory area  510 , for example. Processor  505  may include one or more processing units (e.g., in a multi-core configuration) for executing instructions. The instructions may be executed within a variety of different operating systems on the server system  501 , such as UNIX, LINUX, Microsoft Windows®, etc. It should also be appreciated that upon initiation of a computer-based method, various instructions may be executed during initialization. Some operations may be required in order to perform one or more processes described herein, while other operations may be more general and/or specific to a particular programming language (e.g., C, C#, C++, Java, or other suitable programming languages, etc.). 
     Processor  505  is operatively coupled to a communication interface  515  such that server system  501  is capable of communicating with a remote device such as a user system or another server system  501 . For example, communication interface  515  may receive requests from user system  214  via the Internet, as illustrated in  FIGS. 2 and 3 . 
     Processor  505  may also be operatively coupled to a storage device  334 . Storage device  334  is any computer-operated hardware suitable for storing and/or retrieving data. In some embodiments, storage device  334  is integrated in server system  501 . For example, server system  501  may include one or more hard disk drives as storage device  334 . In other embodiments, storage device  334  is external to server system  501  and may be accessed by a plurality of server systems  501 . For example, storage device  334  may include multiple storage units such as hard disks or solid state disks in a redundant array of inexpensive disks (RAID) configuration. Storage device  334  may include a storage area network (SAN) and/or a network attached storage (NAS) system. 
     In some embodiments, processor  505  is operatively coupled to storage device  334  via a storage interface  520 . Storage interface  520  is any component capable of providing processor  505  with access to storage device  334 . Storage interface  520  may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any component providing processor  505  with access to storage device  334 . 
     Memory area  510  may include, but are not limited to, random access memory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM). The above memory types are exemplary only, and are thus not limiting as to the types of memory usable for storage of a computer program. 
       FIG. 6  illustrates example sets of data used by aggregation computing device  212  (shown in  FIG. 2 ) to manage associations of merchants  132  to aggregate merchants  130  (shown in  FIG. 1 ). Aggregation computing device  212  includes a pool of rule antecedents  600  that include a set of individual rule antecedents  602 . Pool of rule antecedents  600 , in the example embodiment, include antecedents  602  such as tiered merchant ID, acquiring ID, merchant DBA name, oil brand code, merchant address, acquiring Interbank Card Association (“ICA”), Merchant Category Code (“MCC”), and merchant tax ID. Alternatively any other rule antecedent  602  that enables the operation of the systems and methods described herein may be used. In some embodiments, rule antecedents  602  are associated with fields and/or field types in a database, such as database  220  (shown in  FIG. 2 ). In operation, in the example embodiment, antecedents  602  are used to construct rules for automating the association of merchants  132  to aggregate merchants  130 , described in greater detail below. 
     Further, in the example embodiment, aggregation computing device  212  includes merchant association rules  620 . Each rule  620  is built out of one or more rule antecedents  602 , and relate to elements of merchant identification, profile information, and/or transaction information involving the merchant. Further, each rule  620  includes a “target value” for an antecedent which, generally, represents a presumptively accurate value for an aspect of a particular aggregate merchant, such as an actual billing address, or the aggregate merchant&#39;s assigned MCC code. By way of example, consider merchant association “rule # 1 ”  622  for “Aggregate Merchant # 1 ”. Rule  622  includes three rule antecedents  602 , one for merchant address  630 , a second for short DBA name  634 , and a third for MCC code  638 . Further, each rule antecedent  630 ,  634 , and  638  within rule  622  also includes an associated value, such as values  632 ,  636 , and  640 . Rule  622 , in the example, includes three associated values: for merchant address  630 , the associated value  632  is “2233 Rainbow St.”; for short DBA name  634 , the associated value  636  is “WALLYSTROUTSHOP”; and for MCC code  638 , the associated value  640  is “1234”. Further, each rule antecedent  602  within rules  620  may, in some embodiments, have an associated weight, such as weights  633 ,  637 , and  641 . In operation, the rule antecedent/value/weight combinations (e.g.,  630 / 632 / 633 ,  634 / 636 / 637 , and  638 / 640 / 641 ) are used by aggregation computing device  212  when applying rule  622  during merchant association operations, described in greater detail below. 
       FIG. 7  illustrates example data sets used by aggregation computing device  212  (shown in  FIG. 1 ) during merchant association analysis. Aggregation computing device  212  includes data structures representing aggregate merchants  130 , such as “aggregate merchant # 1 ”  702 , and their associated merchants  132 . Aggregation computing device  212  also includes a set of rules  620 , such as the set of merchant rules  620  described above in reference to  FIG. 6 . Further, aggregation computing device  212  includes one or more merchants  710 . In some embodiments, merchants  710  are “orphaned merchants”  710  (i.e., merchants that are not associated with an aggregate merchant, but that should be). Each merchant  710  includes merchant data  712  which, in some embodiments, is data derived either directly or indirectly from transactions such as payment card transactions  126  (shown in  FIG. 1 ), or otherwise relates to the identification and/or classification of a merchant, such as merchant profile information. Merchant data  712 , in some embodiments, includes data such as described in relation to pool of rule antecedents  600  (shown in  FIG. 6 ). 
     During operation, in the example embodiment, aggregation computing device  212  examines a merchant  710 , also referred to herein as an “orphaned merchant”  710 , and attempts to find possible associations between merchant  710  and aggregate merchants  130 . As used herein, the term “orphaned merchant” refers generally to a merchant that should be associated with one or more aggregate merchants  130 , but is not. In some cases, merchant  710  may not actually be orphaned (e.g., when merchant  710  is not supposed to be associated with any aggregate merchant  130 ). In other cases, merchant  710  is orphaned, and thus needs to be associated with one or more aggregate merchants  130 . In both cases, aggregation computing device  212  examines merchant  710  and attempts to find potential matches. It should be understood that while this example embodiment describes analyzing a single merchant with respect to many aggregate merchants (one merchant to many aggregate merchants), it is also possible to analyze a single aggregate merchant with respect to many merchants (many merchants to one aggregate merchant), as well as many merchants to many aggregate merchants. 
     In order to find potential associations between merchant  710  and aggregate merchants  130 , aggregation computing device  212 , during operation, applies rules  620  to merchant data  712 . Merchant data  712 , in some embodiments, may also be considered and referred to as “merchant location data” (i.e., data related to a merchant location). For example, presume aggregation computing device  212  is examining an example merchant  710  that includes merchant data  712  as shown in  FIG. 7 . In the example, merchant data  712  includes a short DBA name of “WALLYSFISHSHOP”, a merchant address of “2233 Rainbow Street”, and an MCC code of “1234”. Such data may have been identified by aggregation computing device  212  from a payment card transaction consummated using a POS device  218  (shown in  FIG. 2 ) and, as such, merchant data  712  may include data from one or more transactions sent by POS device  218 . Presume also, for example, that merchant  710  is orphaned (i.e., not already associated with any aggregate merchants  130 ). It should be understood that while, in this example, the merchant  710  has no existing associations with aggregate merchants  130  within aggregation computing device  212 , merchant  710  could have an existing association, and aggregation computing device  212 , in some embodiments, may still examine potential associations with aggregate merchants  130  (i.e., having an already-existing association does not prohibit aggregation computing device  212  from performing association analysis and recommendations against an already-associated merchant, and there may be a motivation to search for wrongly-associated merchants). 
     Aggregation computing device  212  compares merchant data  712  to one or more merchant association rules  620 . For example, considering again “Aggregate Merchant # 1 , Rule # 1 ”  622 , during comparison, merchant data  712  would be compared against rule  622 . More specifically, each antecedent of rule  622  is compared to the corresponding data in merchant data  712  and examined for similarity. The comparison of each element of rule  620  to its corresponding element of merchant data  712 , in the example embodiment, yields a value between 0.0 and 1.0 (0 representing least similarity, 1 representing most similarity). These individual similarity values are referred to herein as the “confidence sub-values” associated with the particular field of merchant data  712 . For example, comparison of the rule&#39;s  622  merchant address  630  (e.g., “2233 Rainbow St.”) to the merchant&#39;s  710  address in merchant data  712  (e.g., “2233 Rainbow Street”) would yield a single confidence sub-value for that field. Moreover, the merchant address is an imperfect match, but very similar, and thus should yield a value indicating substantial similarity (i.e., a value near 1.0). 
     Similarity comparisons between rule fields and merchant data fields are, in the example embodiment, categorized into three different comparison types: (a) equality, (b) tiered comparison, and (c) hybrid (fields that fall somewhere in between). In the example embodiment, (a) antecedents “tiered merchant ID”, “merchant tax ID”, “oil brand code”, and “acquiring ICA” utilize equality comparison, (b) “short DBA name”, “merchant address”, and “acquiring ID” utilized tiered comparison, and (c) “MCC” and “geography” utilize a hybrid approach. 
     The first comparison type, in the example embodiment, is equality comparison. During comparison, one field of a rule, such as rule  622 , is compared with the corresponding field of the orphaned merchant, such as orphaned merchant  710 . With equality comparison, either the comparison fields are equal or they are not, resulting in a confidence sub-value of 1 (equal) or 0 (not equal). For example, fields “acquiring ID”, “tiered merchant ID”, “merchant tax ID”, “oil brand code”, and “acquiring ICA” are fields that are evaluated with the equality comparison. Either the comparison fields are equal, or they are not. Thus, these fields generate either a confidence sub-value of either 1.0 or 0.0. 
     The second comparison type, in the example embodiment, is a tiered comparison. With tiered comparison, the fields being compared may be equal, and thus would generate a confidence sub-value of 1.0. However, under tiered comparison, unlike equality comparison, unequal fields may not necessarily generate a 0.0 confidence sub-value. A tiered comparison will have two or more tiers (other than complete equality), each having an associated score to assign as the confidence sub-value if the comparison meets the parameters of the particular tier. For example, in some embodiments, the “MCC” field is treated with tiered comparison. If the rule and merchant “MCC” fields (antecedents) match, they are assigned a score of 1.0. If they do not match, then other proximity information for the MCC is analyzed. For example, if the rule MCC value and the merchant MCC value are in the same industry, then a score of 0.6 is assigned. If they are not in the same industry, but if they are in the same sector, then a score of 0.3 is assigned. If they are not in the same industry or sector, then a score of 0.0 is assigned. In this example embodiment, similarity information other than the character information present within the fields is utilized (e.g., tables correlating industry and sector categorization for MCC codes). In other embodiments, geography is similarly applied, with proximity of geographic regions used as the measure of similarity. It should be understood that other tiering is possible for both MCC and geography, and that tiering is also possible for other antecedents/fields, including the other antecedents  602  used in the example embodiments of this disclosure. 
     The third comparison type, in the example embodiment, is a fuzzy comparison. In some embodiments, fuzzy comparison based on n-gram comparison of strings is used to evaluate data for character similarity. In this embodiment, the analysis involves an approximate matching methodology described in U.S. Pat. No. 8,219,550, issued 10 Jul. 2012 to Merz, et al., which is hereby incorporated by reference in its entirety. Using the n-gram approach to fuzzy comparison of strings, aggregation computing device  212  compares the rule information for a particular antecedent to the merchant information for the corresponding antecedent. For example, the merchant address of rule  622  (i.e., the string “2233 Rainbow St.”) would be compared to the merchant address of orphaned merchant  710  (i.e., the string “2233 Rainbow Street”). Most of the n-grams of each string are identical, because the strings are nearly identical. The n-gram comparison generates a value between 0.0 and 1.0, with 0.0 representing no similarity, and 1.0 representing approximately identical similarity. The n-gram comparison value is then assigned as the confidence sub-value for the particular field compared. 
     This fuzzy comparison methodology utilizes a technique of string comparison that captures, mathematically, the linguistic concept of “nearness”, as applied to biller names and addresses. For example, consider an example comparison of an individual&#39;s names and addresses (for illustrative purposes): 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Example Transaction Record Data Fields 
               
            
           
           
               
               
               
               
               
               
            
               
                 RECNUM 
                 NAME 
                 STREET ADDRESS 
                 CITY 
                 STATE 
                 ZIP 
               
               
                   
               
               
                 1 
                 Wally 
                 909 N 10th St 
                 Boise 
                 ID 
                 83702 
               
               
                   
                 Lo Faro 
               
               
                 2 
                 Walter 
                 909 North 10th Street 
                 Stanley 
                 ID 
                 83706 
               
               
                   
                 LoFaro 
               
               
                   
               
            
           
         
       
     
     These transactions may refer to the same biller, “Walter Lo Faro” of Idaho, but the only exact match is the state. In some embodiments, the data may be standardized by common methods known in the art in order to facilitate better string comparison. In the example embodiment, address standardization is an algorithm implemented in SAS that uses a database table to standardize tokens parsed from the address. City name standardization uses postal codes to assign preferred city names corresponding to one or more zip codes in proximity to each other. Further, both names and addresses are converted to all capital letters. For example, after some common standardization techniques are applied, the two transactions may be stored as: 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Example Standardized Transaction Record Data Fields 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                 STREET 
                   
                   
                   
               
               
                 RECNUM 
                 NAME 
                 ADDRESS 
                 CITY 
                 STATE 
                 ZIP 
               
               
                   
               
               
                 1 
                 WALLY LO 
                 909 N 10TH ST 
                 BOISE 
                 ID 
                 83702 
               
               
                   
                 FARO 
               
               
                 2 
                 WALTER 
                 909 N 10TH ST 
                 BOISE 
                 ID 
                 83706 
               
               
                   
                 LOFARO 
               
               
                   
               
            
           
         
       
     
     After standardization, string comparison begins. In the example embodiment, “n-grams” are used to compare name and address information between the payee information (i.e., the transaction information) and biller information. An n-gram, generally, is a substring of length n. More specifically, n-grams are used to break up a string into constituent components that may be used for further analysis. In some embodiments, n-grams of length “2” are used (i.e., “2-grams”). For example, the name “WALLY” generates the following 2-grams: “_W”, “WA”, “AL”, “LL”, “LY”, and “Y_”. It should be understood, however, that other length n-grams may be used may be used with this system. 
     Further, in the example embodiment, 2-grams may be converted to a mathematical integer. For example, let&#39;s suppose out character set contains 37 elements: the alphabet, the digits, and the space. Then there would be 37*37=1369 possible 2-grams. Order the 2-grams AA, AB, . . . , AZ, A0, . . . , A9, A_, BA, . . . , B_, . . . ,_A, . . . ,_. For any string define the string&#39;s 2-gram vector representation to be the vector:
 
 v =( v   1   ,v   2   , . . . ,v   1369 )
 
where v i =the number of times the i th  2-gram appears in the string. For WALLY we have:
 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 Example 2-gram Order for “WALLY” 
               
            
           
           
               
               
               
            
               
                   
                 2-gram 
                 2-gram&#39;s order 
               
               
                   
                   
               
            
           
           
               
               
               
            
               
                   
                 WA 
                 851 
               
               
                   
                 AL 
                 12 
               
               
                   
                 LL 
                 456 
               
               
                   
                 LY 
                 469 
               
               
                   
                   
               
            
           
         
       
     
     The 2-gram vector representation of WALLY is then the vector v above with all of the components v i =0 except for i in {12, 456, 469, 851} where v i =1. Representing v in the data as an array consisting primarily of 0s is wasteful. In practice, there are rows in a data set for each nonzero 2-gram (the v i  from above). 
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                 Example Term Count for 2-grams in “WALLY” 
               
            
           
           
               
               
               
               
            
               
                   
                 String 
                 2-gram 
                 term_count 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                   
                 WALLY 
                 AL 
                 1 
               
               
                   
                 WALLY 
                 LL 
                 1 
               
               
                   
                 WALLY 
                 LY 
                 1 
               
               
                   
                 WALLY 
                 WA 
                 1 
               
               
                   
                 ABAB 
                 AB 
                 2 
               
               
                   
                 ABAB 
                 BA 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     No information is lost during this process. Conversion back to the vector representation v is possible. In operation, we want to know if, for a given name and address pair, whether a 2-gram came from the name or the address. Below are the rows for the data in our original example. 
     
       
         
           
               
             
               
                 TABLE 5 
               
             
            
               
                   
               
               
                 Example 2-grams for Two Example Payees 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                   
                 2- 
                 term 
                 term 
                   
                 2- 
                 term 
                 term 
               
               
                 recnum 
                 gram 
                 type 
                 count 
                 recnum 
                 gram 
                 type 
                 count 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 1 
                 _W 
                 1 
                 1 
                 2 
                 _W 
                 1 
                 1 
               
               
                 1 
                 WA 
                 1 
                 1 
                 2 
                 WA 
                 1 
                 1 
               
               
                 1 
                 AL 
                 1 
                 1 
                 2 
                 AL 
                 1 
                 1 
               
               
                 1 
                 LL 
                 1 
                 1 
                 2 
                 LT 
                 1 
                 1 
               
               
                 1 
                 LY 
                 1 
                 1 
                 2 
                 TE 
                 1 
                 1 
               
               
                 1 
                 Y —   
                 1 
                 1 
                 2 
                 ER 
                 1 
                 1 
               
               
                 1 
                 _L 
                 1 
                 1 
                 2 
                 R —   
                 1 
                 1 
               
               
                 1 
                 LO 
                 1 
                 1 
                 2 
                 _L 
                 1 
                 1 
               
               
                 1 
                 O —   
                 1 
                 2 
                 2 
                 LO 
                 1 
                 1 
               
               
                 1 
                 _F 
                 1 
                 1 
                 2 
                 OF 
                 1 
                 1 
               
               
                 1 
                 FA 
                 1 
                 1 
                 2 
                 FA 
                 1 
                 1 
               
               
                 1 
                 AR 
                 1 
                 1 
                 2 
                 AR 
                 1 
                 1 
               
               
                 1 
                 RO 
                 1 
                 1 
                 2 
                 RO 
                 1 
                 1 
               
               
                 1 
                 _9 
                 2 
                 1 
                 2 
                 O —   
                 1 
                 1 
               
               
                 1 
                 90 
                 2 
                 1 
                 2 
                 _9 
                 2 
                 1 
               
               
                 1 
                 09 
                 2 
                 1 
                 2 
                 90 
                 2 
                 1 
               
               
                 1 
                 9 —   
                 2 
                 1 
                 2 
                 09 
                 2 
                 1 
               
               
                 1 
                 _N 
                 2 
                 1 
                 2 
                 9 —   
                 2 
                 1 
               
               
                 1 
                 N —   
                 2 
                 1 
                 2 
                 _N 
                 2 
                 1 
               
               
                 1 
                 _1 
                 2 
                 1 
                 2 
                 N —   
                 2 
                 1 
               
               
                 1 
                 10 
                 2 
                 1 
                 2 
                 _1 
                 2 
                 1 
               
               
                 1 
                 0T 
                 2 
                 1 
                 2 
                 10 
                 2 
                 1 
               
               
                 1 
                 TH 
                 2 
                 1 
                 2 
                 0T 
                 2 
                 1 
               
               
                 1 
                 H 
                 2 
                 1 
                 2 
                 TH 
                 2 
                 1 
               
               
                 1 
                 _S 
                 2 
                 1 
                 2 
                 H 
                 2 
                 1 
               
               
                 1 
                 ST 
                 2 
                 1 
                 2 
                 _S 
                 2 
                 1 
               
               
                 1 
                 T —   
                 2 
                 1 
                 2 
                 ST 
                 2 
                 1 
               
               
                   
                   
                   
                   
                 2 
                 T —   
                 2 
                 1 
               
               
                   
               
            
           
         
       
     
     In one embodiment, a measure of string similarity using 2-grams would simply be to count the number of 2-grams shared by two strings. For WALLY and WALTER, this would equal 2. For example, the strings ABAC, ABACC, ABACCC, . . . all share two 2-grams with ABA but each string in the sequence is less similar to ABA than the preceding one. The measure of string similarity may be refined to take into account strings of varying length. Further, the measure may be rescaled (divide it by something) so that identical strings have similarity equal to 1. For example: 
               similarity   ⁢           ⁢     (       string   ⁢           ⁢   1     ,     string   ⁢           ⁢   2       )       =       1   ⇒   1     =         Number   ⁢           ⁢   of   ⁢           ⁢   2     -     grams   ⁢           ⁢   in   ⁢           ⁢   common           (     junk   ⁢           ⁢   involving   ⁢           ⁢   string   ⁢           ⁢   1     )     ⁢     (     junk   ⁢           ⁢   involving   ⁢           ⁢   string   ⁢           ⁢   2     )                 
If string1 and string2 are the same then they have the same number of 2-grams, say n of them.
 
               ⇒     n       (     junk   ⁢           ⁢   involving   ⁢           ⁢     string   ⁢   1       )     2         =       1   ⇒       (     junk   ⁢           ⁢   involving   ⁢           ⁢     string   ⁢   1       )     2       =       n   ⇒     (     junk   ⁢           ⁢   involving   ⁢           ⁢     string   ⁢   1       )       =     n               
In one embodiment, a measure of string similarity is defined as:
 
     
       
         
           
             
               similarity 
               ⁢ 
               
                   
               
               ⁢ 
               
                 ( 
                 
                   
                     string 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                   
                   , 
                   
                     string 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     2 
                   
                 
                 ) 
               
             
             = 
             
               
                 
                   number 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   of 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   shared 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   2 
                 
                 - 
                 grams 
               
               
                 
                   
                     
                       
                         
                           number 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           of 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           2 
                         
                         - 
                         
                           grams 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           in 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           string 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           1 
                         
                       
                     
                   
                 
                 
                   
                     
                       
                         
                           number 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           of 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           2 
                         
                         - 
                         
                           grams 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           in 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           string 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           2 
                         
                       
                     
                   
                 
               
             
           
         
       
     
     Identical strings now have similarity equal to 1. Further, for example, the similarity of “WALTER” and “WALLY” is approximately 0.447. 
     The above is one embodiment of a string similarity function. In another embodiment, the possibility of repeated 2-grams may be addressed, as well as strings sharing uncommon 2-grams, like CZ should be more similar than those only sharing common ones like TH or LE. Suppose we have two strings to compare and they have 2-gram vector representations:
 
 v =( v   i   ,v   2   , . . . ,v   1369 )
 
 u =( u   1   ,u   2   , . . . ,u   1369 )
 
     Our previous versions of string similarity have not used the vector components. If you look you will not see any u i  anywhere in the formulas. Remember that u i  is the total number of occurrences of the i th  2-gram in the string so incorporating it into our formula will address the first issue. Here is another embodiment of a measure of similarity: 
     
       
         
           
             
               similarity 
               ⁢ 
               
                   
               
               ⁢ 
               
                 ( 
                 
                   
                     string 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                   
                   , 
                   
                     string 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     2 
                   
                 
                 ) 
               
             
             = 
             
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   1369 
                 
                 ⁢ 
                 
                   
                     u 
                     i 
                   
                   ⁢ 
                   
                     v 
                     i 
                   
                 
               
               
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       1369 
                     
                     ⁢ 
                     
                       u 
                       i 
                       2 
                     
                   
                 
                 ⁢ 
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       1369 
                     
                     ⁢ 
                     
                       v 
                       i 
                       2 
                     
                   
                 
               
             
           
         
       
     
     The new numerator deals with repeated 2-grams while the new denominator rescales it so identical strings still have a similarity of 1. Further, this may be described as the cosine of the angle between the vectors u and v. The numerator is the dot product (inner product) of the vectors and the denominator is the product of their lengths. This puts our similarity in the realm of linear algebra and we can now bring to bear all of the tools of the field on the matching problem. For example, the un-weighted 2-gram similarity between ‘ABABC’ and ‘ABD’ is 0.57735027, and between ‘ABABC’ and ‘ABABD’ is 0.83333333. 
     For the second issue, an idea from the field of text mining may be used. As it stands now, each 2-gram makes an equal contribution into the similarity score. Another way to say this is that the 2-grams are equally weighted. The basic idea behind term frequency-inverse document frequency (TF/IDF) weighting is that the highest weighted 2-grams are those that occur most often in a small set of strings. 
     The term frequency for a 2-gram in a string is something we&#39;ve already seen and we have several equivalent ways to describe it: (a) it is the number of times a 2-gram occurs in a string; (b) it is the field term_count in the example data above; and (c) it is the component v i  from the vector representation of the string, where the 2-gram in question is the i th  one. Inverse document frequency is the adjustment we give those weights to account for a 2-gram&#39;s relative uniqueness in the master data set and it is defined as: 
     
       
         
           
             
               
                 IDF 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 of 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 the 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 2 
               
               - 
               
                 gram 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 XY 
               
             
             = 
             
               
                 log 
                 10 
               
               ⁢ 
               
                 
                   total 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   number 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   of 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   strings 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   in 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   the 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   master 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   data 
                 
                 
                   1 
                   + 
                   
                     number 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     of 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     strings 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     containing 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     XY 
                   
                 
               
             
           
         
       
     
     Here XY stands for any 2-gram. The rarer XY is in the master set of strings, the smaller the denominator and since the numerator is constant we get what we wanted: rarer 2-grams yielding larger weights. The “1+” is there to avoid potential division by 0 issues; it does not affect the value of the weight significantly. The logarithm is there to reduce the range of possible weights and to smooth them out. We can now define the TF/IDF-weighted 2-gram vector representation of a string as:
 
 v =( v   1   ,v   2   , . . . ,v   1369 )
 
where:
 
 v =(term frequency of  i   th 2−gram in the string)*(IDF of  i   th 2−gram in the master)
 
     In plain English, all we&#39;ve done multiply the term by term the weights by the appropriate IDFs. Finishing our example based on the name “WALLY LO FARO”: 
     
       
         
           
               
             
               
                 TABLE 6 
               
             
            
               
                   
               
               
                 Example 2-gram Weights 
               
            
           
           
               
               
               
               
               
               
               
               
               
               
            
               
                 rec 
                   
                 term 
                 term 
                 2-gram 
                 rec 
                   
                 term 
                 term 
                 2-gram 
               
               
                 num 
                 2-gram 
                 type 
                 count 
                 weight 
                 num 
                 2-gram 
                 type 
                 count 
                 weight 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
            
               
                 1 
                 _W 
                 1 
                 1 
                 1.34157 
                 2 
                 _W 
                 1 
                 1 
                 1.34157 
               
               
                 1 
                 WA 
                 1 
                 1 
                 1.42929 
                 2 
                 WA 
                 1 
                 1 
                 1.42929 
               
               
                 1 
                 AL 
                 1 
                 1 
                 0.82517 
                 2 
                 AL 
                 1 
                 1 
                 0.82517 
               
               
                 1 
                 LL 
                 1 
                 1 
                 0.93024 
                 2 
                 LT 
                 1 
                 1 
                 1.44582 
               
               
                 1 
                 LY 
                 1 
                 1 
                 1.50429 
                 2 
                 TE 
                 1 
                 1 
                 0.91807 
               
               
                 1 
                 Y —   
                 1 
                 1 
                 0.86584 
                 2 
                 ER 
                 1 
                 1 
                 0.64556 
               
               
                 1 
                 _L 
                 1 
                 1 
                 1.06145 
                 2 
                 R —   
                 1 
                 1 
                 0.91512 
               
               
                 1 
                 LO 
                 1 
                 2 
                 1.19318 
                 2 
                 _L 
                 1 
                 1 
                 1.06145 
               
               
                 1 
                 O —   
                 1 
                 1 
                 1.17492 
                 2 
                 LO 
                 1 
                 1 
                 1.19318 
               
               
                 1 
                 _F 
                 1 
                 1 
                 1.14269 
                 2 
                 OF 
                 1 
                 1 
                 1.43168 
               
               
                 1 
                 FA 
                 1 
                 1 
                 1.54156 
                 2 
                 FA 
                 1 
                 1 
                 1.54156 
               
               
                 1 
                 AR 
                 1 
                 1 
                 0.75258 
                 2 
                 AR 
                 1 
                 1 
                 0.75258 
               
               
                 1 
                 RO 
                 1 
                 1 
                 1.03955 
                 2 
                 RO 
                 1 
                 1 
                 1.03955 
               
               
                 1 
                 _9 
                 2 
                 1 
                 1.95164 
                 2 
                 O —   
                 1 
                 1 
                 1.17492 
               
               
                 1 
                 90 
                 2 
                 1 
                 1.49506 
                 2 
                 _9 
                 2 
                 1 
                 1.95164 
               
               
                 1 
                 09 
                 2 
                 1 
                 1.67481 
                 2 
                 90 
                 2 
                 1 
                 1.49506 
               
               
                 1 
                 9 —   
                 2 
                 1 
                 1.11125 
                 2 
                 09 
                 2 
                 1 
                 1.67481 
               
               
                 1 
                 _N 
                 2 
                 1 
                 0.86686 
                 2 
                 9 —   
                 2 
                 1 
                 1.11125 
               
               
                 1 
                 N —   
                 2 
                 1 
                 0.63231 
                 2 
                 _N 
                 2 
                 1 
                 0.86686 
               
               
                 1 
                 _1 
                 2 
                 1 
                 1.02324 
                 2 
                 N —   
                 2 
                 1 
                 0.63231 
               
               
                 1 
                 10 
                 2 
                 1 
                 0.90418 
                 2 
                 _1 
                 2 
                 1 
                 1.02324 
               
               
                 1 
                 0T 
                 2 
                 1 
                 0 
                 2 
                 10 
                 2 
                 1 
                 0.90418 
               
               
                 1 
                 TH 
                 2 
                 1 
                 1.03733 
                 2 
                 0T 
                 2 
                 1 
                 0 
               
               
                 1 
                 H 
                 2 
                 1 
                 1.04732 
                 2 
                 TH 
                 2 
                 1 
                 1.03733 
               
               
                 1 
                 _S 
                 2 
                 1 
                 0.36729 
                 2 
                 H 
                 2 
                 1 
                 1.04732 
               
               
                 1 
                 ST 
                 2 
                 1 
                 0.47176 
                 2 
                 _S 
                 2 
                 1 
                 0.36729 
               
               
                 1 
                 T —   
                 2 
                 1 
                 0.89096 
                 2 
                 ST 
                 2 
                 1 
                 0.47176 
               
               
                   
                   
                   
                   
                   
                 2 
                 T —   
                 2 
                 1 
                 0.89096 
               
               
                   
               
            
           
         
       
     
     Thus, the comparison of names generates a value, or confidence sub-value, of 0.762256. (The addresses are identical, so their similarity is equal to 1). 
     While the example embodiment describes using an n-gram approach to string comparison, other string comparison methods that enable operation of the systems and methods described herein may be used. Further, in some embodiments, only a portion of an antecedent and/or field may be used during comparison. For example, a rule value, such as value  632 , may only contain a substring of the associated antecedent, such as a partial street address, or a partial short DBA name. 
     Once aggregation computing device  212  has generated confidence sub-values for a particular rule&#39;s antecedents as compared to a particular orphaned merchant, such as between rule  622  and orphaned merchant  710 , the sub-values are combined. For rules having only a single antecedent, there is no combination of sub-values because there is only one sub-value (i.e., the single confidence sub-value generated from the single antecedent). For rules configured with more than one antecedent, in the example embodiment, the multiple sub-values are combined by weighting the sub-values. Each rule antecedent may have a weight assigned to it, such as weight  633 , “½”, weight  637 , “¼”, and weight  641 , “¼”. Alternatively, each rule antecedent within rules  620  may be equally weighted. System applies the weights, such as weights  633 ,  637 , and  641 , to the respective confidence sub-values, thus adjusting the confidence sub-values prior to combining them. It should be understood that weights are shown in  FIGS. 6 and 7  in fractional notation for illustrative purposes, thus “½” is equivalent to 0.5 decimal, and “¼” is equivalent to 0.25 decimal. Further, it should be understood that weighting may be accomplished by other methods, such as not using a separate weight, but instead using different scales of values for each individual antecedent relative to the other antecedents, such as, for example, valuing one antecedent between 0.0 and 5.0 and another field between 0.0 and 2.0. 
     In the example embodiment, rule  622  defines a weight  633 , “½” (or decimal 0.5), to the merchant street address  630  antecedent. As such, whatever sub-value is generated by the comparison operation will be multiplied by the antecedent&#39;s associated weight. In the example shown, the sub-value for merchant street address  630  would be multiplied by 0.5. Similarly, the sub-values for short DBA name  634  and MCC code  638  would each be multiplied by 0.25 (“¼”, as defined by their respective weights  637  and  641 ). Thus, the weighting of merchant street address  630  sub-value is twice the relative weight of short DBA name  634  and MCC code  638  sub-values. Once each sub-value is weighted, the resulting weighted sub-values are added together to provide a weighted confidence value. Aggregation computing device  212  uses the weighted confidence value as the measure of similarity between rule  622  and orphaned merchant  710 . 
     In some embodiments, rule types (not shown) are associated with rules  620 . Each rule type identifies a particular set of antecedents that may help identify certain merchants  132  to their associated aggregate merchant  130 . As such, a user of aggregation computing device  212  may configure a set of rule types that may be used for various aggregate merchants  130  as templates. Rule values for each antecedent present, such as values  632 ,  636 , and  640 , may still need to be identified. In some embodiments, weights such as weight  633 ,  637 , and  641  may be standard with the rule type, and/or the weights may be customizable by the user. In the example embodiment, the following rule types of “standard”, “tiered”, “tax”, “acquirer”, “manual”, and “oil” are implemented with the marked antecedents: 
     
       
         
           
               
             
               
                 TABLE 7 
               
             
            
               
                   
               
               
                 Rule Types 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 Stan- 
                   
                   
                 Ac- 
                   
                   
               
               
                   
                 dard 
                 Tiered 
                 Tax 
                 quirer 
                 Manual 
                 Oil 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Ante- 
                 oil brand code 
                   
                   
                   
                   
                   
                 x 
               
               
                 cedent 
                 short dba name 
                 x 
                 x 
                 x 
                   
                 X 
                 x 
               
               
                   
                 address 
                   
                   
                   
                   
                 X 
               
               
                   
                 acquiring id 
                   
                   
                   
                 x 
               
               
                   
                 geography 
                 x 
                   
                 x 
               
               
                   
                 tiered id 
                   
                 x 
               
               
                   
                 tax id 
                   
                   
                 x 
               
               
                   
                 mcc 
                 x 
                 x 
                 x 
                 x 
                   
                 x 
               
               
                   
                 ica 
                   
                   
                   
                 x 
               
               
                   
               
            
           
         
       
     
     Rule  622 , as shown in  FIG. 7 , is a “standard” type rule. Rule  622 , in the illustrated example, includes antecedents merchant address  630 , short DBA name  634 , and MCC code  638  from the “standard” rule type. The user has entered values  632 ,  636 , and  640  for each antecedent. Further, while the “standard” rule type was configured with equal weighting (i.e., one third for each of the three antecedents present), the user has modified the weighting to more heavily weigh similarity of merchant address over the other two antecedents present (i.e., merchant address  630  weighted with “0.5”, and both short DBA name  634  and MCC code  638  weighted with “0.25”). 
     In some embodiments, rules  620  and/or rule types may also define the type of comparison operation (i.e., the comparison type) that aggregation computing device  212  will use for each particular antecedent. For example, rule  622  is configured to use the third comparison type, fuzzy comparison, for both merchant address  630  and short DBA name  634 , but to use the second comparison type, hybrid comparison, for comparing MCC code  638 . Each comparison generates a sub-value between 0.0 and 1.0, which is then weighted and combined, as described above. It should be understood that, while the example comparisons described herein generate sub-values between 0.0 and 1.0, other value ranges are possible without deviating from the scope of the present disclosure. In the example embodiments described herein, the sub-values range restrictions assist with relative weighting. 
     In some embodiments, aggregation computing device  212  may limit the set of merchants  710  analyzed, or the set of rules  620  used to generate comparison values. For example, a user of aggregation computing device  212  may want to analyze only a certain aggregate merchant, such as aggregate merchant  702 . That aggregate merchant may have one or more rules associated with itself, thereby identify the rule set to use. Further, the user may want to analyze all merchants  710  available to aggregation computing device  212 , or the user may wish to limit to just orphaned merchants, thereby defining the merchant set to use. The merchant set may even include merchants  132  already assigned to aggregate merchant  702 . After identifying the rule set and merchant set, aggregation computing device  212  would then apply each of the rules to each of the merchants as described above, thereby generating comparison scores for each merchant as to each rule of the aggregate merchant. These scores may be used by, for example, a human auditor who analyzes the scores and makes a final determination as to which merchants are associated with which aggregate merchants, or the system itself may associate particular merchants based on highest scorers or scores above a certain pre-determined thresholds. 
       FIG. 8  is an example method  800  for associating related merchants implemented using the aggregation computing system shown in  FIGS. 2 and 3 . Method  800 , in the example embodiment, includes identifying  810  an association rule for an aggregate merchant, such as rule  622  for aggregate merchant  702 . Rule  622  includes one or more antecedents  602  (shown in  FIG. 6 ), such as antecedents  630 ,  634 , and  638  (shown in  FIG. 7 ), each antecedent including a model value for the antecedent associated with the aggregate merchant, such as values  632 ,  636 , and  640  (shown in  FIG. 7 ). In some embodiments, rule  622  includes weights for each antecedent present, such as weights  633 ,  637 , and  641  (shown in  FIG. 7 ). In other embodiments, rule  622  includes a comparison type (not shown) for one or more antecedents present in rule  622 . In still other embodiments, comparison types are assigned to an antecedent within pool of antecedents  600  (shown in  FIG. 7 ) such that the comparison type for the antecedent is inherited from pool of antecedents  600  and not defined within individual rules such as rule  622 . And in still other embodiments, comparison types may be assigned within pool of antecedents  600  as a default comparison type, but may also be defined within individual rules such as rule  622  such that the rule&#39;s comparison type for an antecedent will trump the default comparison type as defined for the antecedent within pool of antecedents  600 . 
     Further, in the example embodiment, method  800  includes identifying  820  one or more merchant data values associated with a merchant, such as data values  712  associated with merchant  710 . Merchant data values  712  have individual fields (antecedents) that correspond to an antecedent within rule  622 . For example, “WALLYSFISHSHOP” is an example merchant data value within merchant data values  712  which corresponds with antecedent “short DBA name”  634  (shown in  FIG. 7 ) having a model value of “WALLYSTROUTSHOP”  636  (shown in  FIG. 7 ). 
     Also, in the example embodiment, method  800  includes applying  830  the association rule, such as rule  622 , to the one or more merchant data values, such as merchant data values  712 . In some embodiments, applying  830  includes comparing the model value for each antecedent with a merchant data value associated with the corresponding antecedent. As such, a confidence score  832  is generated for the merchant, the confidence score representing a likelihood of association between the merchant, such as merchant  710 , and the aggregate merchant, such as aggregate merchant  702 . Confidence score  832  is then output  840  to, for example, a user of aggregation computing device  212 . In other embodiments, method  800  also includes aggregation computing device  212  selecting a highest confidence score from a plurality of confidence scores and assigning that merchant with the appropriate aggregate merchant. 
       FIG. 9  illustrates additional steps for associating related merchants that, in some embodiments, are used for applying  830  the association rule to the merchant data value as shown in  FIG. 8 . In some embodiments, method  800  (shown in  FIG. 8 ) includes generating  910  a plurality of confidence sub-values  912 . Each confidence sub-value  912  corresponds to an antecedent of the one or more antecedents within rule  622 . For example, comparison of merchant address  630  (shown in  FIG. 7 ) from rule  622  (i.e., using value  632 ) with the merchant address “2233 Rainbow Street” from merchant data values  712  generates a single confidence sub-value  912 . Likewise, comparison of short DBA name and MCC code between rule  622  and merchant data values  712  generate two additional confidence sub-values  912 . 
     Also, in some embodiments, applying  830  includes weighing  920  each sub-value  912  with an associated weight prior to combining  930 . In some embodiments, the associated weight may be defined by the antecedent definition within the rule, such as weight  633  for merchant address  630  within rule  622 . In other embodiments, the associated weight may be defined by aggregation computing device  212  using a default policy, or may be defined by pool of antecedents  600 . In the example embodiment, an individual sub-value  912  is multiplied by an associated weight, such as weight  633 , to generate a plurality of weighted sub-values  922 . 
     Further, in some embodiments, applying  830  includes combining  930  the plurality of confidence sub-values  912  or weighted sub-values  922  to generate confidence score  832 . In the example embodiment, combining  930  includes adding weighted sub-values  922  together to generate confidence score  832 . 
       FIG. 10  is an example method  1000  for building rules and sets of rules that may be used to associate related merchants in accordance with the present disclosure. In some embodiments, aggregation computing device  212  (shown in  FIG. 2 ) performs the steps of method  1000 . In the example embodiment, an aggregate merchant is identified  1010  for analysis, such as “aggregate merchant # 1 ”  1012 . Aggregate merchant  1012  includes a set of merchants  1014  already associated with itself. In some embodiments, merchants  1014  are “known-good” (or presumed-good) merchants of aggregate merchant  1012  (i.e., an analyst using method  1000  has examined merchants  1014  and believes they are all properly associated with this particular aggregate merchant  1012 ). 
     Also in the example embodiment, one or more sets of patterns  1022  are generated  1020 . The term “pattern”, as used herein, refers to a potential rule (i.e., a pattern is a proposed rule that has not yet been selected to actually use as a rule). Patterns are discussed herein in terms of whether or not they would assign particular merchants to a particular aggregate merchant, but they are not used to actually assign merchants to aggregate merchants (like rules may be). A pattern, such as pattern  1024 , has a similar structure to the rules described above, such as rules  620  (shown in  FIG. 7 ). In other words, a pattern consists of one or more antecedents, each antecedent having an associated value. In some embodiments, like rules, a pattern also includes a comparison type. In other embodiments, patterns  1024  are identical to rules  620 . Pattern sets  1022 , therefore, represent potential sets of rules that may be used to identify merchants that belong to an aggregate merchant, such as merchants  1014  to aggregate merchant  1012 . Ideally, as discussed above, an accurate rule set will be a set of rules that properly associates only the merchants that actually belong to an aggregate merchant, and exclude those that do not. Method  1000  helps analyze the performance of possible rule sets (i.e., pattern sets) to see which rule sets perform best. 
     In some embodiments, pattern sets  1022  are generated  1020  manually, such as by human analysts. In the example embodiment, pattern sets  1022  are generated  1020  automatically by aggregation computing device  212  using the steps set forth below, and may then be selected for use by human analysts or by aggregation computing device  212 . 
     For any given aggregate merchant, such as aggregate merchant  1012 , there are many different qualifying pattern sets possible, and often no obvious best. For example, since merchant DBA name, MCC code, and country code are normally available for a location (i.e., merchant), a set consisting of standard (and possibly manual) rules is one possibility. When present, the tiered merchant ID may be used. If an aggregate merchant is affiliated with gas stations, oil brand code is a possible antecedent to include, and so on. In the example embodiment, in order to make the determination as to what types of patterns  1024  to include in a pattern set  1022 , the merchants known to be associated with the aggregate merchant are analyzed. The distributions of variables, such as tiered merchant ID, merchant tax ID, etc., are examined, and those that exceed a utility threshold are used. For example, this analysis might result in an example instruction such as “build  3  pattern sets—(1) standard patterns (with possible manual patterns), (2) tiered merchant ID and standard patterns (with possible manual patterns), and (3) tiered merchant ID, tax ID, and standard patterns (with possible manual patterns).” 
     Thus, for each pattern set  1022 , several patterns  1024  are built. Each pattern  1024 , like the rules described above, will consist of one or more antecedents, along with associated values for each antecedent. The pattern types are defined by the example instruction above, but the values for each antecedent need to be defined. In the example embodiment, the patterns generated by aggregation computing device  212  all have some elements of commonality: (1) they all consider the merchant DBA name as an antecedent, and they can do this in two ways—by considering a sub string of the antecedent, or by requiring equality for the rule to be satisfied; (2) they all consider another field in the antecedent that may be “wildcarded” (i.e., ignored); (3) they all consider a third field in the antecedent that may not be wildcarded. For example, consider the following: 
     
       
         
           
               
             
               
                 TABLE 8 
               
               
                   
               
               
                 Example Pattern Set 
               
               
                   
               
             
            
               
                 ANTECEDENT 
               
            
           
           
               
               
               
               
            
               
                 MERCHANT_COMPARE_NAME 
                 COMPARE_BEGIN_POSITION 
                 COMAPRE_LENGTH 
                 SUBSTRING_RULE 
               
               
                   
               
               
                 WALLYSTROUT 
                 1 
                 11 
                 YES 
               
               
                 WALLYSTROUT 
                 1 
                 11 
                 YES 
               
               
                 WALLYSTROUTSHOP 
                   
                   
                 NO 
               
               
                 WALLYSTROUTSHOP 
                   
                   
                 NO 
               
               
                   
               
            
           
           
               
               
               
            
               
                   
                 ANTECEDENT 
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 MERCHANT_COMPARE_NAME 
                 COUNTRY_CODE 
                 MCC_CODE 
                 AGGREGATE_MERCHANT_ID 
               
               
                   
                   
               
               
                   
                 WALLYSTROUT 
                 USA 
                 5555 
                 12345 
               
               
                   
                 WALLYSTROUT 
                 * 
                 5555 
                 12345 
               
               
                   
                 WALLYSTROUTSHOP 
                 USA 
                 5555 
                 12345 
               
               
                   
                 WALLYSTROUTSHOP 
                 * 
                 5555 
                 12345 
               
               
                   
                   
               
            
           
         
       
     
     The first example pattern would assign every location (i.e., merchant) in the United States of America (country_code=USA) with MCC=5555 that has “WALLYSTROUT” as the first 11 characters of its merchant DBA name to an aggregate merchant “12345” (i.e., aggregate_merchant_id=12345). The second example pattern would ignore country_code, and thus would assign all merchants with MCC=555 having “WALLYSTROUT” as the first 11 characters of its merchant DBA name to the same example aggregate merchant. The third example pattern would assign every location in the USA with MCC=5555 whose merchant DBA name is equal to “WALLYSTROUTSHOP” to the same aggregate merchant. And the fourth example pattern is similar to the third pattern, just ignoring country code. 
     Referring again to  FIG. 10 , once one or more sets of patterns  1022  have been generated  1020 , a set of patterns is analyzed  1030 . In the example embodiment, an initial run flag is provided. If  1040  the initial run flag is set to “yes”, then outer and inner frequencies are computed  1050  for every pattern  1024 , as described in more detail below. If  1040  the initial run flag is set to “no”, then method  1000  updates  1052  frequencies. 
     In the example embodiment, method  1000  further includes computing  1060  the desirability of all patterns  1024 , discussed in greater detail below. Method  1000  also includes adding  1070  the most desirable rules to the rule set and deleting it from the pattern set. Further, if  1080  there are locations not qualified by a rule yet, then the process of updating  1052  frequencies is repeated until there are none remaining. Adding  1070  a rule causes the inner and outer frequencies of the other patterns to possibly change (e.g., the same location could be qualified by more than one pattern). Method  1000  then outputs  1090  the rule set for use. 
     As such, conditional  1040  enables method  1000  to avoid some re-computing  1050  operations. For example, if a first pattern qualifies 12 total locations, 10 of which it should have qualified, 2 of which it should not have, then that first pattern has an inner frequency of 10 and an outer frequency of 12. As such, any other pattern that qualifies any of those 12 locations will need to have its inner and outer frequencies adjusted (i.e., updated  1052 ). In one embodiment, instead of re-computing the entire inner and outer frequencies, detailed logic and set theory may be used to adjust the inner and outer frequencies already computed  1052 . 
     With regard to computing  1050  inner and outer frequencies for a pattern, in the example embodiment, each pattern is analyzed individually. The inputs to analysis include: (1) a pattern type (i.e., rule type); (2) a set of location data (i.e., merchant data, such as merchant data  712  (shown in  FIG. 7 ), for all merchants  1014  associated with aggregate merchant  1012 ); (3) a non-wildcarded variable (such as MCC code in the examples above); (4) a wildcarded variable (such as country code in the examples above); and (5) initial run flag (i.e., a flag indicating if the pattern type is the first in the pattern set or not—used for housekeeping purposes). Further, in the example embodiment, all of the n-grams are determined for the merchant DBA name. In the example embodiments described herein, all of the patterns output by aggregation computing device  212  use merchant DBA name as an antecedent, and only examines substrings starting in position  1  (i.e., compare-begin_position=1). It should be understood, however, that these are merely examples, and that other embodiments and pattern generation is anticipated by this disclosure. Other types of merchant data may be suitable to n-gram comparison. 
     For each pattern, an inner frequency is computed. As used herein, the term “inner frequency” is defined as the number of already-associated merchants that the pattern “qualifies” (i.e., looking only at the merchants already associated with the aggregate merchant, how many of these would this pattern match to the aggregate merchant). As used herein, when a pattern is referred to as “qualifying” a particular merchant for an aggregate merchant, this means that the pattern would associate that merchant with the aggregate merchant being analyzed (i.e., if this pattern was made a rule and used, it would associate the merchant with the aggregate merchant). Also as used herein, the term “over-qualification” refers to when a pattern qualifies a particular merchant for an aggregate merchant, but that merchant should not be associated with the aggregate merchant (i.e., the pattern would wrongly qualify that merchant). 
     Also for each pattern, in the example embodiment, an outer frequency is computed. As used herein, the term “outer frequency” is defined as the total number of merchants (already-associated or not) that the pattern would qualify. To distinguishing inner frequency from outer frequency, inner frequency analyzes only a pool of merchants already associated with an aggregate merchant, where outer frequency analyzes a larger pool of merchants, regardless of whether or not they are currently associated with the aggregate merchant. In some embodiments, unnecessary patterns are eliminated. For example, consider the following two patterns: 
     
       
         
           
               
             
               
                 TABLE 9 
               
               
                   
               
               
                 Additional Example Patterns 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                 MERCHANT_COMPARE_NAME 
                 COMPARE_BEGIN_POSITION 
                 COMAPRE_LENGTH 
                 SUBSTRING_RULE 
               
               
                   
               
               
                 WALLYSTROUT 
                 1 
                 11 
                 YES 
               
               
                 WALLYSTROUTS 
                 1 
                 11 
                 YES 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 MERCHANT_COMPARE_NAME 
                 COUNTRY_CODE 
                 MCC_CODE 
                 INNER_FREQ 
               
               
                   
                   
               
               
                   
                 WALLYSTROUT 
                 USA 
                 5555 
                 10 
               
               
                   
                 WALLYSTROUTS 
                 USA 
                 5555 
                 10 
               
               
                   
                   
               
            
           
         
       
     
     The first pattern may be discarded. Both patterns qualify 10 locations in the aggregate merchant, and the outer frequency of the first pattern is guaranteed to be greater than or equal to the second pattern. In other words, the first pattern is at best as good as the second pattern, and may be worse due to more potential over-qualifications. 
     With regard to computing  1060  a desirability value of patterns, in the example embodiment, the desirability value gives a numeric approximation for evaluating the strength of patterns. In some embodiments, better patterns qualify more merchants and leave few out. One example formula that is used for desirability value is: 
             desirability   =       inner   ⁢           ⁢   frequency       1   +     outer   ⁢           ⁢   frequency     -     inner   ⁢           ⁢   frequency               
Where the extra 1 in the denominator is present simply to avoid division by 0 issues for patterns that do not over-qualify any locations.
 
     In some embodiments, the selection of patterns is an iterative process, where the most desirable pattern is selected, the inner and outer frequencies are recomputed, and the process is repeated. When there are no more locations remaining, the loop ends. 
     In other embodiments, manual rules may be created for inclusion in a rule set for an aggregate merchant. Manual rules are used to create a direct assignment of one particular merchant to one particular aggregate merchant. Manual rules may be used to manage a small number of merchants for which there seems to be no good rule to properly qualify them. Manual rules come in two varieties, inclusive and exclusive, and include two antecedents, location ID and aggregate merchant ID. Some rules may over-qualify a small number of locations. Thus, it may be beneficial to write an “exclusion” rule that specifically excludes those locations. Generally speaking, exclusive manual rules are desirable when you need fewer of them than the inner frequency of the rule. This need may be expressed mathematically as “inner frequency&lt;outer frequency&lt;2(inner frequency)”. Similarly, a rule set may qualify all of the merchants that should be associated with an aggregate merchant except a few. Thus, it may be beneficial to write an “inclusion” rule that specifically includes those locations that should be included, but that were not qualified by the rule set. This need may be expressed mathematically as “outer frequency≤2(inner frequency)”. Since these rules are inclusive, the undesirable rule may be deleted from the rule set that they are replacing. 
     Referring to  FIG. 8  and method  800 , in some embodiments, the steps shown in  FIG. 10  may be used to identify  1010  rules for aggregate merchants, such as rule  622  and aggregate merchant  702 . 
       FIG. 11  shows an example configuration  1100  of a database  1120  within a computing device  1110 , along with other related computing components, that may be used to associate related merchants. In some embodiments, computing device  1110  is similar to aggregation computing device  212  (shown in  FIG. 2 ). Database  1120  may be coupled to several separate components within computing device  1110 , which perform specific tasks. 
     In the example embodiment, database  1120  includes association rules data  1122 , aggregate merchant and merchant data  1124 , and merchant transactions data  1126 . In some embodiments, database  1120  is similar to database  220  (shown in  FIG. 2 ). Association rules data  1122  includes information associated with association rules, such as rules  620  (shown in  FIG. 6 ). Aggregate merchant and merchant data  1124  includes data related to aggregate merchants, such as aggregate merchants  130  (shown in  FIG. 7 ), and merchants, such as merchants  132  and  710  (shown in  FIG. 7 ). Transactions data  1126  includes transactions and other information associated with, for example, payment card transactions originating from merchants  132  and  710 , and other types of transactions. 
     Computing device  1110  includes the database  1120 , as well as data storage devices  1130 . Computing device  1110  also includes a rules component  1140  for identifying, administering, generating, or otherwise interacting with association rules, such as rules  622 , within the association rules data  1122  in database  1120 . Computing device  1110  also includes a transactions component  1150  for identifying merchant information from transactions, and the merchant transactions data  1126 . A comparison component  1160  is also included for comparing rules with merchant data and generating comparison values and sub-values. A processing component  1180  assists with execution of computer-executable instructions associated with the merchant association system. 
     The above-described embodiments of methods and systems of associating related merchants for an aggregate merchant. The system identifies rules associated with an aggregate merchant and applies those rules to merchant and/or merchant data to generate relative scores that indicate a likelihood of whether or not each particular merchant is associated with the given aggregate merchant. As a result, the methods and systems described herein facilitate associating individual merchants with an aggregate merchant so that individual merchants&#39; affiliations with aggregate merchants may be known and used during other computing operations. 
     As will be appreciated based on the foregoing specification, the above-described embodiments of the disclosure may be implemented using computer programming or engineering techniques including computer software, firmware, hardware or any combination or subset thereof, wherein the technical effect is identifying, in memory, an association rule for an aggregate merchant, the association rule including one or more antecedents, each antecedent including a model value for the antecedent associated with the aggregate merchant, identifying, in memory, one or more merchant data values associated with a merchant, the one or more merchant data values each corresponding to an antecedent of the one or more antecedents, applying, by a computing device, the association rule to the one or more merchant data values by comparing the model value for each antecedent with a merchant data value associated with the corresponding antecedent, thereby generating a confidence score for the merchant, the confidence score representing a likelihood of association between the merchant and the aggregate merchant, and outputting the confidence score. Any such resulting program, having computer-readable code means, may be embodied or provided within one or more computer-readable media, thereby making a computer program product (i.e., an article of manufacture) according to the discussed embodiments of the disclosure. The computer-readable media may be, for example, but is not limited to, a fixed (hard) drive, diskette, optical disk, magnetic tape, semiconductor memory such as read-only memory (ROM), and/or any transmitting/receiving medium such as the Internet or other communication network or link. The article of manufacture containing the computer code may be made and/or used by executing the code directly from one medium, by copying the code from one medium to another medium, or by transmitting the code over a network. 
     These computer programs (also known as programs, software, software applications, “apps”, or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refers to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The “machine-readable medium” and “computer-readable medium,” however, do not include transitory signals. In other words, the machine-readable medium and the computer-readable medium described herein are non-transitory. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. 
     This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.