Abstract:
A system, software module, and computer program product for performing clustering based data mining that improved performance in model building, good integration with the various databases throughout the enterprise, flexible specification and adjustment of the models being built, and flexible model arrangement and export capability. The software module for performing clustering based data mining in an electronic data processing system comprises: a model setup block operable to receive client input including information specifying a setup of a clustering data mining models, generate the model setup, and generate parameters for the model setup based on the received information, a modeling algorithms block operable to select and initialize a clustering modeling algorithm based on the generated model setup, a model building block operable to receive training data and build a clustering model using the training data and the selected clustering modeling algorithm and a model scoring block operable to receive scoring data and generate predictions and/or recommendations using the scoring data and the clustering model.

Description:
FIELD OF THE INVENTION 
     The present invention relates to a clustering module that generates and scores clustering data mining models. 
     BACKGROUND OF THE INVENTION 
     Data mining is a technique by which hidden patterns may be found in a group of data. True data mining doesn&#39;t just change the presentation of data, but actually discovers previously unknown relationships among the data. Data mining is typically implemented as software in or in association with database systems. Data mining includes several major steps. First, data mining models are generated based on one or more data analysis algorithms. Initially, the models are “untrained”, but are “trained” by processing training data and generating information that defines the model. The generated information is then deployed for use in data mining, for example, by providing predictions of future behavior based on specific past behavior. 
     Data mining is a compute intensive and complex task. Enterprise data mining, that is, data mining that is performed using all or substantial portions of the data generated by an enterprise, requires the mining of very large datasets. Such datasets may include millions of records and it may take hours or even days to build a single model based on such a dataset. Clustering models are an important family of machine learning algorithms that are quite expensive, in terms of computing required, to build when large datasets are used. Clustering is the process of grouping data into classes or clusters. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. A cluster of data objects can be treated collectively as one group in many applications. 
     Problems arise when attempts are made to utilize current data mining systems to perform enterprise data mining. Current systems that perform clustering analysis tend to provide inadequate performance for large datasets, and in particular, do not provide scalable performance. This leads to it taking hours or even days to build a single model. In the context of enterprise data mining, a wide variety of models must be generated to meet specific, but widely different needs throughout the enterprise. A typical enterprise has a variety of different databases from which data is drawn in order to build the models. Current systems do not provide adequate integration with the various databases throughout the enterprise. Likewise, current systems provide limited flexibility in terms of specifying and adjusting the model being built to meet specific needs. Likewise, the various models that are built must be arranged so as to operate properly on the particular system within the enterprise for which the models were built. Current systems provide limited model arrangement and export capability. 
     A need arises for a technique by which cluster analysis may be performed that provides improved performance in model building, good integration with the various databases throughout the enterprise, flexible specification and adjustment of the models being built, and flexible model arrangement and export capability. 
     SUMMARY OF THE INVENTION 
     The present invention is a system, software module, and computer program product for performing clustering based data mining that provides improved performance in model building, good integration with the various databases throughout the enterprise, flexible specification and adjustment of the models being built, and flexible model arrangement and export capability. The software module for performing clustering based data mining in an electronic data processing system comprises: a model setup block operable to receive client input including information specifying a setup of a clustering data mining models, generate the model setup, and generate parameters for the model setup based on the received information, a modeling algorithms block operable to select and initialize a clustering modeling algorithm based on the generated model setup, a model building block operable to receive training data and build a clustering model using the training data and the selected clustering modeling algorithm and a model scoring block operable to receive scoring data and generate predictions and/or recommendations using the scoring data and the clustering model. 
     The software module may further comprise a data preprocessing block operable to receive the training data, process the received training data, and transmit the processed training data to the model building block. The processing performed by the data preprocessing block may comprise normalization of data and/or binning of continuous data into categories. 
     The software module may further comprise a model integration block operable to integrate the clustering model with scoring data. The software module may further comprise a model analysis block operable to statistically analyze the clustering model. The software module may further comprise a status monitoring block operable to monitor a model-building progress of the model building block and output notification of the model-building progress of the model building block. The model building block may be further operable to monitor the client input for an interrupt. The model building block may be further operable to, in response to receiving an interrupt, abort the model build or checkpoint the model build. The model building block may be further operable to periodically checkpoint a model build. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The details of the present invention, both as to its structure and operation, can best be understood by referring to the accompanying drawings, in which like reference numbers and designations refer to like elements. 
         FIG. 1  is an exemplary block diagram of a data mining system, in which the present invention may be implemented. 
         FIG. 2  is an exemplary block diagram of a database/data mining system shown in  FIG. 1 . 
         FIG. 3  is an exemplary block diagram of a database/data mining system  102  shown in  FIG. 1 . 
         FIG. 4  is an exemplary block diagram of a clustering module for data mining shown in  FIG. 3 . 
         FIG. 5  is an exemplary data flow diagram of a model building process performed by the clustering module shown in  FIG. 4 . 
         FIG. 6  is an exemplary data flow diagram of a model scoring process performed by the clustering module shown in  FIG. 4 . 
         FIG. 7  is an exemplary flow diagram of processing performed by the clustering module shown in  FIG. 4 . 
         FIG. 8  is an exemplary format of a training data table that may be used by the clustering module shown in  FIG. 4 . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     An exemplary data mining system  100 , in which the present invention may be implemented, is shown in  FIG. 1 . System  100  includes a database/data mining system  102  that is connected to a variety of sources of data. For example, system  102  may be connected to a plurality of internal or proprietary data sources, such as systems  104 A– 104 N. Systems  104 A– 104 N may be any type of data source, warehouse, or repository, including those that are not publicly accessible. Examples of such systems include inventory control systems, accounting systems, scheduling systems, etc. System  102  may also be connected to a plurality of proprietary data sources that are accessible in some way over the Internet  108 . Such systems include systems  106 A– 106 N, shown in  FIG. 1 . Systems  106 A– 106 N may be publicly accessible over the Internet  108 , they may be privately accessible using a secure connection technology, or they may be both publicly and privately accessible. System  102  may also be connected to other systems over the Internet  108 . For example, system  110  may be privately accessible to system  102  over the Internet  108  using a secure connection, while system  112  may be publicly accessible over the Internet  108 . 
     The common thread to the systems connected to system  102  is that the connected systems all are potential sources of data for system  102 . The data involved may be of any type, from any original source, and in any format. System  102  has the capability to utilize and all such data that is available to it. 
     An exemplary embodiment of database/data mining system  102  is shown in  FIG. 2 . System  102  is a database management system that includes data mining functionality. Database management system  202  is connected to data sources  204 , such as the proprietary and public data sources shown in  FIG. 1 . Database management system includes two main components, data  206 , and database management system (DBMS) engine  208 . Data  206  includes data, typically arranged as a plurality of data tables, such as relational data tables, as well as indexes and other structures that facilitate access to the data. DBMS engine  208  typically includes software that receives and processes queries of the database, obtains data satisfying the queries, and generates and transmits responses to the queries. DBMS engine  208  also includes data mining block  210 , which provides DBMS engine  208  with the capability to obtain data and perform data mining processing on that data, so as to respond to requests for data mining processed data from one or more users, such as user  212 . 
     An exemplary block diagram of a database/data mining system  102 , shown in  FIG. 1 , is shown in  FIG. 3 . Database/data mining system  102  is typically a programmed general-purpose computer system, such as a personal computer, workstation, server system, and minicomputer or mainframe computer. Database/data mining system  102  includes processor (CPU)  302 , input/output circuitry  304 , network adapter  306 , and memory  308 . CPU  302  executes program instructions in order to carry out the functions of the present invention. Typically, CPU  302  is a microprocessor, such as an INTEL PENTIUM® processor, but may also be a minicomputer or mainframe computer processor. Input/output circuitry  304  provides the capability to input data to, or output data from, database/data mining system  102 . For example, input/output circuitry may include input devices, such as keyboards, mice, touchpads, trackballs, scanners, etc., output devices, such as video adapters, monitors, printers, etc., and input/output devices, such as, modems, etc. Network adapter  306  interfaces database/data mining system  102  with network  310 . Network  310  may be any standard local area network (LAN) or wide area network (WAN), such as Ethernet, Token Ring, the Internet, or a private or proprietary LAN/WAN. 
     Memory  308  stores program instructions that are executed by, and data that are used and processed by, CPU  302  to perform the functions of the database/data mining system  102 . Memory  308  may include electronic memory devices, such as random-access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), flash memory, etc., and electro-mechanical memory, such as magnetic disk drives, tape drives, optical disk drives, etc., which may use an integrated drive electronics (IDE) interface, or a variation or enhancement thereof, such as enhanced IDE (EIDE) or ultra direct memory access (UDMA), or a small computer system interface (SCSI) based interface, or a variation or enhancement thereof, such as fast-SCSI, wide-SCSI, fast and wide-SCSI, etc, or a fiber channel-arbitrated loop (FC-AL) interface. 
     Memory  308  includes data  206 , database management processing routines  312 , data mining processing routines  314 , and operating system  316 . Data  206  includes data, typically arranged as a plurality of data tables, such as relational database tables, as well as indexes and other structures that facilitate access to the data. Database management processing routines  312  are software routines that provide database management functionality, such as database query processing. Data mining processing routines  314  are software routines that implement the data mining processing performed by the present invention. In particular, data mining processing routines  314  include clustering based software module (clustering module)  318 , which performs the clustering based data mining of the present invention. Preferably, this data mining processing is integrated with database management processing. For example, data mining processing may be initiated by receipt of a database query, either in standard SQL or in the form of extended SQL statements, or data mining processing may be initiated from a programming language, such as JAVA. Operating system  320  provides overall system functionality. 
     A functional block diagram of a clustering module  318  for data mining, according to the present invention, is shown in  FIG. 4 . Clustering module  318  receives input such as client input  404  and training data  406  and interacts with scoring data  408 . Model setup block  410  receives client input  404  that includes information specifying setups of clustering data mining models. For example, client input  404  may include information specifying a number of clusters to be used in a data mining model, a type of clustering model to be built, such as a self-organizing map, a k-means model, a competitive learning model, etc., and other parameters that are specific to the type of model selected. Model setup block  410  generates the model setups that are used in building the models and generates appropriate parameters for the model setup based on the received information. 
     Data preprocessing block  412  receives training data  406 , preprocesses the training data, and transmits the processed data to model building block  416 . Thus, data preprocessing block processes the training data before the data is used to build a model. For example, numeric columns within training data  406  may be normalized to restrict the range of the data or to eliminate outliers. Likewise, columns of continuous data may be binned to form categorical columns, which reduces the number of unique values present in the data. Data preprocessing block  412  may perform default or predefined processing, or data preprocessing block  412  may receive client input that includes information defining the bins to be used or defining the type of normalization to be performed. 
     Modeling algorithms block  414  selects and initializes the appropriate modeling algorithm based on the model setup that is generated by model setup block  410 . This provides the capability to generate models that are appropriate for different modeling needs, as specified by the client. Factors such as speed, data visualization, ease of tuning, on-line, incremental learning, and batch learning may be supported. 
     Model building block  416  receives a preprocessed training dataset from data preprocessing block  412  and builds a clustering model using the training dataset and the selected clustering modeling algorithm. Model building block  416  builds the clustering model based on the available data columns in the dataset. Columns that have been marked to be ignored, or that are keys, are ignored. The resulting built model is used by model integration block  418  to integrate the model with scoring data  408  that is contained in other datasets. In particular, each data row in another dataset that has similar columns to the training dataset is marked with an identifier of a cluster that the data in the data row is most similar to. This greatly enhances the performance when the clustering model is used to make predictions. In addition, the clustering model may be deployed into the database system itself, in which case the database system can itself use the model to make predictions. 
     Model building block  416  monitors client input for interrupts to the model building process. Depending upon the nature of the interrupt, model building block  416  may abort the model build or it may checkpoint the model build for later resumption. Checkpointing involves saving the complete state of the model build, and includes saving all information necessary to resume the model build from the point of interruption. In addition to checkpointing in response to a client interrupt, model building block  416  also periodically checkpoints the model build. Should a system failure occur that interrupts the model build, only the work done since the last checkpoint is lost, rather than the entire model build. 
     After a model has been built, model analysis block  420  statistically analyzes the model and validates the dataset. Model analysis block  420  computes statistics on the data represent by each cluster in the model. These statistics may then be used to check if a new dataset was generated by the same data generated mechanism as the dataset used for training the model. 
     Status monitoring block  422  monitors the model-building progress of model building block  416  and periodically outputs to the client  424  notification of that progress. 
     Model scoring block  426  receives a scoring dataset, applies the scoring dataset to the built and integrated model, and generates predictions and/or recommendations using the scoring dataset and the model. 
     A data flow diagram of a model building process, performed by clustering module  318 , shown in  FIG. 4 , is shown in  FIG. 5 . Model building involves building the models, in this case, clustering models, which are used to perform online recommendation and prediction. A configuration  502  defines the information, such as items, products, attributes, etc. that may of interest for the user in a particular universe. A schema  504  defines the types of models that are to be built in specific situations. Client input  404  includes information that allows the user to control the building of clustering data mining models. For example, client input  404  may include information specifying a number of clusters to be used in a data mining model, a type of clustering model to be built, such as a self-organizing map, a k-means model, a competitive learning model, etc., and other parameters that are specific to the type of model selected. The configuration  502 , the schema  504 , and the client input  404  are input to model setup step  410 , which sets up the models for training. In particular, model setup step  410  selects the modeling algorithms  414  that process the training data in order to actually build the models. For example, modeling algorithms  414  may include a self-organizing map (SOM) algorithm  512 , a k-means model algorithm  514 , a competitive learning model algorithm  516 , etc. The algorithms that are to be used to build models are selected by model setup step  1106 - 1  based on the definitions in schema  504 , as specified by the client input  404 . 
     In addition, model setup step  410  generates and sets training parameters  518 . Training parameters  518  are parameters that are input to the algorithms to control how the algorithms build the models. Training data  406  is data that is input to the algorithms that is used to actually build the models. Training parameters  518 , the selected modeling algorithm, and training data  406  are input to model building block  416 . 
     Model building block  416  invokes the selected modeling algorithm, initializes it using the training parameters  518 , processes training data  406  using the modeling algorithm, and generates model  524 . Model  524  includes a representation of the conditions, decisions, or spatial arrangement that makes up an operational model. Typically, a clustering model includes information representing the clusters of the model. In a typical embodiment, each cluster may be represented by a vector, which represents the data centroid, center of mass, mode, or median of the rows of data that are assigned to the cluster. In some embodiments, there may be a rule representation of the clusters. Model  524 , is input to model analysis block  420 , which statistically analyzes the model and validates the dataset. Model analysis block  420  computes statistics on the data represent by each cluster in the model. These statistics may then be used to check if a new dataset was generated by the same data generated mechanism as the dataset used for training the model. Model  524  is also output to model integration block  418 , which integrates model  524  with scoring data that is contained in other datasets. 
     A data flow diagram of a model scoring process is shown in  FIG. 6 . Client input  404  is input to prediction setup step  602 . Client input  404  includes user data and desired results data. User data may include data relating to types predications/recommendations desired by the user, data relating to constraints on the generated predication/recommendation desired by the user, or relating to specific actions the user has taken. Desired results data may include definitions of the types of predictions and recommendations and constraints on the predictions and recommendations desired. For example, user data may include information relating to items the user is purchasing and desired results data may indicate that the desired result is a recommendation for another product to be suggested to the user for purchase. 
     Prediction setup step  602  uses the input user data and desired results data to select models  606 , to select and generate prediction parameters  610 , and to generate prediction data  612 . Models  606  include representations of the clustering models, such as vectors representing centroids, centers of mass, modes, or medians of the rows of data that are assigned to each cluster, which were generated by model building block  416 , shown in  FIG. 5 . Prediction setup step  602  selects a model for use in scoring step  604  based on the user data and on the desired results data included in client input  404 . Prediction parameters  610  are parameters that are input to the scoring step  604  to control the scoring of scoring data  408  against the model and are input to the selection and prediction/recommendation process to control the generation of predictions and recommendations. Prediction setup step  602  selects and generate predication parameters  610  for use in scoring step  604  based on the user data and on the desired results data included in client input  404 . 
     The selected model  614 , prediction parameters  610 , and scoring data  408  are input to scoring step  604 . In scoring step  604 , each row of scoring data  408  is scored according to selected model  614 , as controlled by prediction parameters  610 , to generate one or more scores for each row of scoring data  408 . The scores for each row of data indicate how closely the row of data matches attributes of a cluster, how much confidence may be placed in the prediction, how likely the output prediction/recommendation from the model is likely to be true, and other statistical indicators. The generated scored data  616  is output from scoring step  604  and includes predictions/recommendations  618 , along with the corresponding probabilities  620  for the scored data. 
     The scored model  616  is input to selection and prediction/recommendation generation step  605 , which evaluates the probabilities associated with each record of scored data  616  and outputs predictions/recommendations  622  based on the scored data. Records may be selected based on prediction parameters  610  provided by the user, for example, to filter records that do not meet some probability threshold. The generated predictions/recommendations are output  622  from step  605  for use in any post data mining processing. 
     Processing  700 , including processing performed by data preprocessing block  412  is shown in  FIG. 7 . Process  700  collects and processes data in order to generate data in a form usable by for the data mining processing performed by the present invention. Process  700  begins with step  702 , in which training data  406 , shown in  FIG. 4 , is acquired from the data sources with which the data mining system operates, such as corporate databases, which provide corporate customer data, external databases, which provide complementary customer data, Web transaction database, which provide web transaction and visitor data, and Web server database, which provides web server data. In step  704 , data that is relevant to the desired output from the system is selected from among the data that has been acquired. In step  706 , the selected data is pre-processed to ensure that the data is usable, properly formatted, etc. For example, numeric columns within training data  406  may be normalized to restrict the range of the data or to eliminate outliers. Likewise, columns of continuous data may be binned to form categorical columns, which reduces the number of unique values present in the data. Default or predefined processing may be performed, or client input may be received that includes information defining the bins to be used or defining the type of normalization to be performed. In step  708 , the data tables that are used by the system to build clustering models are built and stored. 
     An exemplary format of a training data table  802  is shown in  FIG. 8 . Data table  802  includes a plurality of rows or records of data, such as records  804 A– 804 N. Each record represents an individual set of data in data table  802 . Each record includes a plurality of fields of data, each field containing an individual piece of data of a defined type and subject matter. When arranged in a tabular format, the fields of the records form columns such as columns  806 A– 806 B, with each column representing a particular type and subject matter of data. For example, in  FIG. 8 , column  806 A represents “NAME” and contains names, column  806 B represents “ADDRESS” and contains addresses. Likewise, record  804 A includes a name field and an address field. In order to build a clustering model from a dataset, a set of records is processed. The data in the columns of the set of records is preprocessed by data preprocessing block  412 , then processed to form the clustering model. 
     It is important to note that while the present invention has been described in the context of a fully functioning data processing system, those of ordinary skill in the art will appreciate that the processes of the present invention are capable of being distributed in the form of a computer readable medium of instructions and a variety of forms and that the present invention applies equally regardless of the particular type of signal bearing media actually used to carry out the distribution. Examples of computer readable media include recordable-type media such as floppy disc, a hard disk drive, RAM, and CD-ROM&#39;s, as well as transmission-type media, such as digital and analog communications links. 
     Although specific embodiments of the present invention have been described, it will be understood by those of skill in the art that there are other embodiments that are equivalent to the described embodiments. Accordingly, it is to be understood that the invention is not to be limited by the specific illustrated embodiments, but only by the scope of the appended claims.