Knowledge Discovery Based on Soft Computing Model in the Area of Data Mining

Soft Computing models play an important role in the field of recognition, classification, data prediction, etc in various application fields. Soft Computing models include fuzzy logic, neural, network, genetic algorithm, particle swarm optimization, Bacterial forging algotithm, classification and clustering, etc., the extraction of hidden information from large database is possible through the techniques of data mining which can be exploited by the methods of soft computing tools. The data mining techniques include association rule, clustering, decision tree for extracting the proper information from the data base. In this paper an effort has been made to compare the performance of a particular soft computing model after the application of association rule in the data base. Under the soft computing model the performance of fuzzy logic, neural network, particle swarm optimization and tabu search has been compared. Based on minimum average error the particular soft computing model has been selected for the availability of information.