关键词: 核模糊C-均值聚类, 改进的混合蛙跳算法, 聚类分析, 数据挖掘

Abstract: Because of the problems of Kernel Fuzzy C-means Clustering algorithm（KFCM） easy falling into local optimality and the sensitivity to initial value, a kernel fuzzy C-means clustering based on Shuffled Frog Leaping Algorithm（SFLA） is presented. But its effect is not satisfactory for the data with larger clusters number and higher dimensions. So adaptive inertia weight is used to update the strategy of SFLA. Then the obtained optimal solution by improved shuffled frog leaping algorithm（ISFLA） is taken as initial clustering centers of KFCM algorithm to optimize initial clustering centers, so as to get the global optimum and overcome the shortcoming of the KFCM algorithm. The results of experiments on the artificial and real data show that compared with the KFCM and FCM clustering algorithm, the new algorithm optimization ability would be stronger, the number of iterations less, and the clustering effect better.

Key words: Kernel Fuzzy C-means Clustering（KFCM）, improved shuffled frog-leaping algorithm, cluster analysis, data mining