摘要： 针对传统FCM算法对孤立点比较敏感，须预先指定聚类数目的缺陷，提出一种新的模糊聚类算法NSFCM，将其应用于文本挖掘中。NSFCM对数据对象的隶属度增加一个权值，以减少孤立点对聚类中心的影响。采用平均信息熵确定聚类数，通过密度函数获得初始聚类中心。仿真结果证明，该算法聚类的精度和执行效率均高于FCM算法，效果较好。

关键词: 聚类数, 文本聚类, 模糊聚类

Abstract: The main defect of traditional methods of FCM algorithm is sensitive to the isolated data and is to know the number of clustering in advance. A fuzzy clustering algorithm NSFCM is presented in this paper, and NSFCM agorithm is applied to text mining. This algorithm adds a weight to the membership of the data, which is to decrease the effect on the initial cluster center. This paper applies average information entropy to find the number of clusters and adopts a density function algorithm to find the initial cluster centers. The experiment shows both the precision and the efficiency of clustering NSFCM are higher than those of FCM.

Key words: number of clustering, text clustering, fuzzy clustering

中图分类号: 

• TP301.6