Multi-threaded Naïve Bayes algorithm for enterprise-server data mining

摘要

The present invention provides an efficient method and system for performing data mining on large amounts of data using a multi-threaded Bayesian algorithm. The method for data mining includes receiving data from a database, performing multi-threaded classification on the data using a Naïve Bayes model and performing multi-threaded regression on the data based on a Naïve Bayes model. One of the multi threaded classification and regression steps may include processing the data according to a batch learning system, processing the data according to an incremental learning system, processing the data according to an online learning system, and processing the data according to a distributed learning system. One of the the multi-threaded classification and regression steps may include developing a set of rules from a set of training data. One of the multi-threaded classification and regression steps may comprise arranging the data into a plurality of groups.