Novel improved fuzzy clustering algorithm applied in network intrusion detection
Cao Dong-xia · Jisuanji gongcheng yu sheji · 2012
When using traditional fuzzy C-Means(FCM),the number of clusters must be given beforehand.Furthermore,the algorithm is sensitive to the isolated data and the original clusters and easy to run into local critical point,and these factors have a great influence on the quality of the final clustering results.Because of the faults,the text uses a mixed search strategy which combines genetic algorithm and tabu search to improve the efficiency of FCM.The strategy possesses the advantages of these algorithms.The purpose of this mechanism is to produce the best number of clusters automatically,optimize the selection of the original cluster and advance FCM's ability of breaking away from local critical point.Experiments show that the improved algorithm,with self adaptiveness and high efficiency,gets better clustering results than the original.