A Genetic Multi-Agent Rule Induction System for Stream Data
Jin-Hwa Kim, Chaehwan Won, Hyeon-su Byeon · 2008
Many data mining algorithms are not capable of working effectively with very large stream data sets. Today's, organizations are building massive amounts of Internet-related stream data they collect, process, and store. Organizations want to mine effectively large stream data sets. But existing data mining algorithms have many critical problems. Storage management, increased run time, complexity of algorithms is the examples. This study constructs a new stream data mining algorithms, and builds knowledge base from very large stream data sets with genetic algorithm and rule induction system. Unlike exiting methods that build knowledge from stream data sets, genetic multi-agent rule induction system builds knowledge from the large stream data sets and then significantly improves prediction and classification accuracy.