Sampling Fuzzy K-Means Clustering Algorithm Based on Clonal Optimization

In data mining field, FCM algorithm is an efficient method in the process of small scale low dimensional database, but the time performance of FCM algorithm can not be satisfied for the large scale high dimensional database. In this paper, a new sampling technique with clonal operation is used in SFCO algorithm to improve the time performance and the quality of clustering. The simulation experiments shows that the SFCO algorithm is an effective method in the data mining of large scale database, in addition it not only avoids the local optima and is robust to initialization, but also evidently restrains the degenerating phenomenon during the evolutionary process.