基于初始聚类中心选取的改进FCM聚类算法  

Improved Fuzzy C Means Clustering Algorithm Based on Selecting Initial Clustering Centers

作　　者：张慧哲[1] 王坚[1] 

机构地区：[1]同济大学CIMS研究中心,上海201804

出　　处：《计算机科学》2009年第6期206-209,共4页Computer Science

基　　金：国家"863"计划基金资助项目(2003AA414120);国家科技支撑计划项目(2006BAF01A46);国家"863"计划基金资助项目(2003AA414120);上海市社会发展重大专项项目(06DZ12001);上海市基础研究重点项目(06JC14066);上海市科技发展基金重点项目(061612058);上海市登山行动计划项目(061111006)资助

摘　　要：针对模糊C-均值(Fuzzy C-Means,FCM)算法聚类效果往往受到初始聚类中心影响,收敛结果易陷入局部极小的问题,提出了一种改进的模糊C均值聚类算法。算法给出了一种简洁快速的初始聚类中心的选取规则,并根据聚类中心的分离特性改进了目标函数,从而使获得的聚类结果为全局最优。仿真结果证明新算法与传统FCM方法相比,迭代次数少,准确率高,同时也更加适用于样本数据分类不均衡的聚类问题。This paper proposed an improved FCM algorithm aiming at many problems in Fuzzy C Means algorithm, such as being sensitive to initial conditions, usually leading to local minimum results. The new algorithm can obtain global optimal solutions through a new simple and efficient select rule of the initial cluster centers, furthermore alternating optimization in terms of a novel separable criterion. By comparative testing with custom FCM, the new algorithms not only have fewer numbers of iterations and have higher accuracy, but also more suitable for problems with not balanced classified samples.

关 键 词：聚类 FCM聚类 目标函数 初始聚类中心 分离度 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]