关键词: 模糊C-均值算法, 加权模糊C-均值算法, 聚类有效性, 模糊聚类分析

Abstract: Fuzzy C-means algorithm is an unsupervised pattern recognition method. Clustering precision of the algorithm is affected by its equal partition trend for data sets. A dot density weighted fuzzy C-means algorithm is proposed by using density size of data dot regarded as weighted value and distributing characteristic of data’s own. The method has not only to certain extent overcome limitation of fuzzy C-means algorithm, but also been favorable convergence. Clustering effect is more improved when clustering has been instructed by supervised information of a few data dots of known clustering. Clustering validity of the algorithm is evaluated by clustering validity function, thereby that has offered theoretic basic for clustering performance of the algorithm.

Key words: fuzzy C-means algorithm, weighted fuzzy C-means algorithm, clustering validity, fuzzy clustering analysis