关键词: 模糊聚类, 模糊点, 相似性距离, 中心区域, 二次聚类

Abstract: Concerning the problem of wrong partition at fuzzy boundary in Fuzzy C-Means (FCM) clustering algorithm, an improved recalculation technique for fuzzy points was proposed. The new method took into account the data distribution characteristics in different classes. Firstly, it made the hyperspheres central regions by clear data, then defined a new similarity distance based on the clear radius of central region to recalculate the membership of fuzzy point, and finally reassigned the fuzzy points to right category. The experimental results show that the new algorithm can correct some wrong partition and improve the definition of fuzzy point, and also it is a promising algorithm for dataset with significant density differences.

Key words: fuzzy clustering, fuzzy point, similarity distance, central region, second clustering