Abstract:
A distributed data mining method and system includes a mediator and a plurality of agents, each of said plurality of agents having a local database. The mediator invokes the agents and each agent performs an attribute/value selection process. The agents pass their respective best attribute/value pair to the mediator and the mediator determines a winning agent from the submissions. The agents are notified of the winning selection and the winner then begins data splitting based on the willing attribute/value pair. The winning agent forwards a split information index to the mediator. The mediator provides the split information index to other, non-winning agents and the agents generate rules for the data mining.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
   The present application claims the benefit of U.S. Provisional Patent Applications Ser. No. 60/394,526 filed Jul. 10, 2002, and Ser. No. 60/394,527 filed Jul. 10, 2002, both of which are incorporated herein by reference. 

   BACKGROUND OF THE INVENTION 
   1. Field of the Invention 
   The present invention relates generally to a data mining method and system and, more specifically, to a distributed agent-based data mining method and system for use in distributed data environments. 
   2. Description of the Related Art 
   As the 21st Century begins, we have seen an explosive growth in capabilities to both generate and collect data. It has been estimated that the amount of data in the world doubles every 20 months and the size and number of databases are increasing even faster. Advances in sensor based data collection as well as the computerization of many area of human activities have flooded decision makers with information and generated an urgent need for new techniques and tools that can intelligently and automatically assist them in transforming this huge amount of data into a useful operational and tactical knowledge. 
   It is recognized that information is at the heart of business operations and that decision-makers should make the best use of data to gain valuable insight into the business. 
   Current database management systems give access to the data stored but this is only a small part of what could be gained from the data. Traditional on-line transaction processing systems, On-Line Transaction Processes (OLTPs), are good at putting data into databases quickly, safely and efficiently but are not good at delivering meaningful analysis in return. Analyzing data can provide further knowledge about a business by going beyond the data explicitly stored to derive knowledge about the business. This is where Data Mining or Knowledge Discovery in Databases (KDD) has obvious benefits for any enterprise. 
   Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. This encompasses a number of different technical approaches, such as clustering, data summarization, learning classification rules, finding dependency net works, analyzing changes, and detecting anomalies. 
   SUMMARY OF THE INVENTION 
   The present invention provides an agent-based data mining method and system for information exploitation in distributed data environments. The invention distributes computationally expensive and data intensive knowledge mining/discovery processes and at the same time providing for (a) highly coupled algorithmic level integration of these processes and (b) executing these processes without any requests for data transfer among them. A compression tool is provided in a preferred embodiment. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a data flow diagram of a distributed data mining method and system according to the principles of the present invention; and 
       FIG. 2  is a process block diagram illustrating the method of the present invention. 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     FIG. 1  illustrates the basic concept behind the present invention. Distributed mining is accomplished via a synchronized collaboration of agents  10  as well as a mediator component  12 . The mediator component  12  facilitates the communication among the agents  10 . As it can be seen in  FIG. 1 , each agent  10  has access to its own local database  14  and is responsible for mining the data contained by the database. 
   Distributed data mining results in a set of rules generated through a tree induction algorithm. The tree induction algorithm, in an iterative fashion, determines the feature which is most discriminatory and then it dichotomizes (splits) the data into classes categorized by this feature. The next significant feature of each of the subsets is then used to further partition them and the process is repeated recursively until each of the subsets contain only one kind of labeled data. The resulting structure is called a decision tree, where nodes stand for feature discrimination tests, while their exit branches stand for those subclasses of labeled examples satisfying the test. A tree is rewritten to a collection of rules, one for each leaf in the tree. Every path from the root of a tree to a leaf gives one initial rule. The left-hand side of the rule contains all the conditions established by the path, and the right-hand side specifies the classes at the leaf. Each such rule is simplified by removing conditions that do not seem helpful for discriminating the nominated class from other classes 
   In the distributed framework, tree induction is accomplished through a partial tree generation process and an agent-mediator communication mechanism ( FIG. 2 ) that executes the following steps: 
   1. The data mining process starts with the mediator  12  issuing a call to all the agents  10  to start the mining process. 
   2. Each agent  10  then starts the process of mining its own local data by finding the feature (or attribute) that can best split the data into the various training classes (i.e. the attribute with the highest information gain). 
   3. The selected attribute is then sent as a candidate attribute to the mediator  12  for overall evaluation. 
   4. Once the mediator  12  has collected the candidate attributes of all the agents  10 , it can then select the attribute with the highest information gain as the winner. 
   5. The winner agent  10  (i.e. the agent whose database  14  includes the attribute with the highest information gain) will then continue the mining process by splitting the data using the winning attribute and its associated split value. This split results in the formation of two separate clusters of data (i.e. those satisfying the split criteria and those not satisfying it). 
   6. The associated indices of the data in each cluster are passed to the mediator  12  to be used by all the other agents  10 . 
   7. The other (i.e. non-winner) agents  10  access the index information passed to the mediator  12  by the winner agent  10  and split their data accordingly. The mining process then continues by repeating the process of candidate feature selection by each of the agents  10 . 
   8. Meanwhile, the mediator  12  is generating the classification rules by tracking the attribute/split information coming from the various mining agents  10 . The generated rules can then be passed on to the various agents  10  for the purpose of presenting them to the user through advanced 3D visualization techniques. 
   According to a further aspect of the invention, a compression method is provided. The method and system according to this aspect of the invention includes a number of data mining agents  10  whose efforts are coordinated through a facilitator. One of the major functions of the facilitator is to collect information from various agents  10  and to broadcast the collected information to other agents  10  involved in the mining process. To this end, there is a certain amount of cost (in terms of resources) associated with the distributed mining process, namely that of the communication bandwidth. For very large datasets, the high cost of transferring information from one agent to another can become a major bottleneck. To alleviate this problem the present invention provides that a compression method is applied in order to reduce the transmission costs. 
   As explained in previously, each agent  10  in is responsible for mining its own local data by finding the feature (or attribute) that can best split the data records into the various training classes (i.e. the attribute with the highest information gain). The selected attribute is then sent as a candidate attribute to the mediator  12  for overall evaluation. Once the mediator  12  has collected the candidate attributes of all the agents, it can then select the attribute with the highest information gain as the winner. The winner agent  10  (i.e. the agent whose database includes the attribute with the highest information gain) will then continue the mining process by splitting the data records using the winning attribute and its associated split value. This split results in the formation of two separate clusters of data records (i.e. those satisfying the split criteria and those not satisfying it). The associated indices of the data records in each cluster are passed to the mediator  12  to be used by all the other agents  10 . The other (i.e. non-winning) agents  10  access the index information passed to the mediator  12  by the winning agent  10  and split their data records accordingly. The mining process then continues by repeating the process of candidate feature selection by each of the agents  10 . 
   Thus, in the present process, the bulk of the information which needs to be passed from one agent  10  to another during the collaborative mining process is comprised of a list of data record indexes. Passing the index information using an integral representation can become a major problem for a large number of data records. To this end, present method utilizes a compression mechanism, which includes the following two processes: 
   Process 1. Index Bit-Vector Generation: During the index bit-vector generation phase, the index information, normally represented as a set of integers (i.e. record numbers), is converted to a bit-vector representation. In a bit-vector representation, each individual bit corresponds to the index of a single data record. Thus bit number three, for example, corresponds to the index of the third data record. The actual value of the bit represents the presence or absence of the corresponding data record in the data cluster being passed from one agent to another. A value of “1” represents the presence of that data record, while a value of “0” represents its absence. This representation is much more compact than the set-of-integers representation. The difference in size for large number of data records is dramatic. 
   Process 2. Index Bit-Vector Compression: Once the index information is converted into a bit-vector representation, a compression algorithm is used to further reduce the size of the data being transferred. Another major advantage of using the index bit-vector representation, over that of using integral values, besides its compactness, is that since the data splitting process is an iterative one, in any given iteration (except the first one), we don&#39;t really need to know the actual (i.e. original) index positions of the data records being split. Instead, all that is required is the information about the presence or absence of any data record in any subsequent split. This makes the size of the index information being passed from one agent to another at each iteration significantly smaller than that of the previous one. 
   Advantages of the present method and system become apparent by comparison to a multi-agent system. While the present method exhibits some resemblance to a mobile Multi Agent System (MAS—a loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver), it differentiates from a MAS in very many aspects. Its major differentiators are explained in 
   
     
       
             
             
             
           
             
             
             
           
         
             
                 
               TABLE 1 
             
             
                 
                 
             
             
                 
                 
               Present Distributed 
             
             
                 
                 
               Knowledge Management 
             
             
                 
               MAS Based Solutions 
               (DKM) Solutions 
             
             
                 
                 
             
           
           
             
                 
             
           
        
         
             
               Computational Mechanism 
               An MAS mainly retrieves, 
               The present method goes beyond 
             
             
                 
               filters, and globally 
               retrieval and filtering by applying 
             
             
                 
               coordinates information from 
               computationally intensive data 
             
             
                 
               sources that are spatially 
               mining processes that can discover 
             
             
                 
               distributed. 
               “deep” knowledge (e.g., rules) across 
             
             
                 
                 
               distributed data locations. Discovered 
             
             
                 
                 
               knowledge represents inductive 
             
             
                 
                 
               generalization of distributed data. 
             
             
                 
                 
               Uniqueness: Distributing 
             
             
                 
                 
               computationally expensive and data 
             
             
                 
                 
               intensive knowledge 
             
             
                 
                 
               mining/discovery processes and at 
             
             
                 
                 
               the same time providing for highly 
             
             
                 
                 
               coupled algorithmic level integration 
             
             
                 
                 
               of these processes without any 
             
             
                 
                 
               requests for data transfers. 
             
             
               Agent Information 
               A typical MAS architecture 
               The present method generalizes from 
             
             
               Exchange Level 
               provides for a data component 
               data. Its information integration 
             
             
                 
               level information extraction via 
               process is conducted on the higher 
             
             
                 
               collaborative filtering, search, etc. 
               level of data models, rather than on 
             
             
                 
               An example of such a data level 
               the data component level. In fact, to 
             
             
                 
               approach is NIMA&#39;s project on 
               data components are exchanged 
             
             
                 
               “Intelligent Database Agents for 
               among mining agents. 
             
             
                 
               Geospatial Knowledge Integration 
               Uniqueness: Support for data security 
             
             
                 
               and Management is concerned the 
               at the algorithimic level (no data 
             
             
                 
               collection and integration of data 
               transfers among agents). 
             
             
                 
               by mobile agents via four distinct 
             
             
                 
               classes of agents-updating, 
             
             
                 
               integration, conflation, and 
             
             
                 
               managing agents. 
             
             
               Knowledge Representation 
               A MAS represents knowledge in 
               A rule generation mechanism in the 
             
             
                 
               the form of filtered data 
               present method is geared towards 
             
             
                 
               components and usually provides 
               generation of predictive models that 
             
             
                 
               for some level of semantic 
               can be used immediately to support 
             
             
                 
               integration. 
               decision-making. 
             
             
                 
               To simplify the information 
               Prior to the mining process the 
             
             
                 
               retrieval process some MAS 
               present method performs syntatic 
             
             
                 
               system use domain ontologies. 
               analysis of distributed databases and 
             
             
                 
                 
               generates meta-data bridges. 
             
             
                 
                 
               Uniqueness: Generation of highly 
             
             
                 
                 
               predictive models from data. 
             
             
               Information Retrieval vs 
               The semantic representations of the 
               In contrast to the MAS 
             
             
               Exploitation 
               context of the information analysis 
               representations, the present 
             
             
                 
               problem in a MAS system are 
               method&#39;s representations 
             
             
                 
               mainly used to support intelligent 
               (inductive generalizations) 
             
             
                 
               information retrieval. 
               truly support information 
             
             
                 
                 
               exploitation. 
             
             
                 
                 
               Uniqueness: Information exploitation 
             
             
                 
                 
               via inductive generalizations from 
             
             
                 
                 
               distributed data. 
             
             
               Collaborative Decision 
               Typically two groups of support 
               Exchange of information during the 
             
             
               Making 
               for decision making: 
               decision making process 
             
             
                 
               (a) Exchange of information 
               Uniqueness: helping the “team” of 
             
             
                 
               before the decision making 
               collaborative agents make a wiser 
             
             
                 
               process begins (basing one&#39;s 
               decision by sharing knowledge and 
             
             
                 
               decision on the data provided 
               expertise while the decision is being 
             
             
                 
               by others) 
               made. 
             
             
                 
               (b) Exchange of information after 
             
             
                 
               the decision making process 
             
             
                 
               ends (adjusting one&#39;s decision 
             
             
                 
               based on that of others) 
             
             
                 
             
           
        
       
     
   
   Although other modifications and changes may be suggested by those skilled in the art, it is the intention of the inventors to embody within the patent warranted hereon all changes and modifications as reasonably and properly come within the scope of their contribution to the art.