Analysis of fuzzy class association rule mining based on Genetic Network Programming

Classification rule mining is a practical data mining technique widely used in real world. In the previous work, we have put forward a fuzzy class association rule mining method based on Genetic Network Programming and applied it to network intrusion detection system which proved its efficiency and advantage. In this paper, a detailed comparison not only between fuzzy class association rule minings(FCARMs) with fixed fuzzy membership functions and with evolved fuzzy membership functions, but also between FCARMs with and without probability node transition are carried out. The aim of this paper is to provide experimental analysis on the characteristics of FCARMs with different implementations. Experimental results conducted on real world database, KDD99Cup and DAPRA98 database from MIT Lincoln Laboratory, are studied to verify the comparison.