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。
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.
出处
《青岛科技大学学报（自然科学版）》
CAS
2011年第2期194-198,共5页
Journal of Qingdao University of Science and Technology:Natural Science Edition
关键词
模糊C均值
粒度思想
密度函数
模糊因子
收敛速度
fuzzy C-means（FCM）
granularity thinking
density function
fuzzy factor
convergent speed
