Using neural network to extract knowledge from database

Abstract

Classification, which involves finding rules that partition a given data set into disjoint groups, is one class of data mining problems. Approaches proposed so far for mining classification rules for large databases are mainly decision tree based on symbolic learning methods. In this paper, we use artificial neural network to mine classification rules. We present a novel approach, called LBSB, composed of two phases to extract rules from artificial neural network and discover knowledge in databases. Some experiments have demonstrated that our method generates rules of better performance than the decision tree approach in noisy conditions.