Abstract: A method for prediction analysis using text categorization is provided. The method includes the steps of: grouping a plurality of text documents into a plurality of classes; selecting a top m most discriminatory terms for each class of documents using statistical based measures; determining for each document the presence or absence of each of the discriminatory terms; learning rule-based models of each class of documents using a rule learning algorithm; determining, for at least a portion of the plurality of documents, if a given learned rule has been satisfied by each respective document; creating a database of the rules associated with documents satisfying the rules; and performing distributed data mining to form a predictive result based on at least a portion of the plurality of documents.

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.