This paper presents a methodology for discovering classification rules in data mining. The attributes defining
the data space can be inadequate, making it difficult to discover high-quality knowledge. In order to solve this problem,
this paper proposes a fuzzy c-means model (FCM) for attribute clustering after preprocessing of that attributes
(features). The Genetic Programming (GP) is used to determine which such features are the most predictive. Then
compare these rules with all the clusters and add the rules which success 80% to knowledge base. Using five well
known datasets held at the UCI repository 1
.