计算机工程与应用2013，Vol.49Issue(4)：141-145,5.DOI:10.3778/j.issn.1002-8331.1107-0207
一种基于改进混合蛙跳的KFCM算法
Kernel fuzzy C-means clustering based on improved shuffled frog leaping algorithm
摘要
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.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Key words
Kernel Fuzzy C-means Clustering(KFCM)/improved shuffled frog-leaping algorithm/cluster analysis/data mining分类
信息技术与安全科学引用本文复制引用
赵小强,刘悦婷..一种基于改进混合蛙跳的KFCM算法[J].计算机工程与应用,2013,49(4):141-145,5.基金项目
甘肃省支撑计划项目(No.090GKCA034) （No.090GKCA034）
甘肃省自然科学基金(No.0916RJZA017,No.1112RJZA028). （No.0916RJZA017,No.1112RJZA028）
