PDF全文下载：2011020194
刘喜梅， 雷达
(青岛科技大学 自动化与电子工程学院，山东 青岛266042)
摘要： 针对传统的模糊C均值聚类(FCM)算法聚类数目难以确定，目标函数收敛速度慢的特点，提出了一种改进的模糊聚类算法，将粒度思想和m-α关系引入FCM模糊聚类算法中，从不同的粒度空间对聚类进行有效性评价，并通过改变m或α的值来影响模糊化程度，进而改变聚类的收敛速度。分别采用FCM与该算法对经典数据集进行聚类对比。结果表明：改进后的聚类算法能够得到合理有效的聚类数目和初始聚类中心，并且具有比传统FCM更快的收敛速度。
关键词： 模糊C均值； 粒度思想； 密度函数； 模糊因子； 收敛速度
中图分类号： TP 181 文献标志码: A
An Improved Fuzzy C-Means Clustering Algorithm
LIU Xi-mei, LEI Da
(College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, China)
Abstract: Because the Fuzzy C-Means (FCM) clustering algorithm is difficult to determine clustering numbers and has a low convergent speed. The improved FCM fuzzy clustering algorithm is proposed by introducing the granularity thinking and the relationship of m-α into the FCM. The clusters are evaluated from different granular spaces. And the clustering convergent speed is enhanced by changing the values of fuzzy factors m and α to affect the fuzzification degree. The improved FCM algorithm and the FCM are used in classical data sets to make a comparison. The results have shown that the proposed algorithm can obtain reasonable and effective clustering numbers, and has a faster convergent rate than the FCM.
Key words:fuzzy C-means(FCM); granularity thinking; density function; fuzzy factor; convergent speed
收稿日期:2010-11-16
作者简介: 刘喜梅(1961—)，女，教授.
