关键词: 聚类分析, 模糊聚类, 可能性模糊聚类, 特征逐减

Abstract: Fuzzy C-Means（FCM） algorithm and Possibilistic Fuzzy C-Means（PFCM） algorithm are sensitive to noise points because they do not consider the contribution of data features and individual data points. The Feature-Reduction Fuzzy C-Means（FRFCM） algorithm can remove the useless features of a dataset and compute the feature weights of remainders. So the FRFCM algorithm has better clustering performance. Based on the PFCM algorithm, a new Feature-Reduction Possibilistic Fuzzy C-Means（FRPFCM） algorithm is proposed. The FRPFCM algorithm not only solves the parameter dependency problem of the PFCM algorithm, but also can automatically weed out invalid data features and update the contribution degree to clustering of the rest data features. The experimental results on the synthetic and UCI datasets show that the proposed FRPFCM algorithm can get higher clustering precisions and need less iterations so that speed up its convergence rate.

Key words: clustering analyses;fuzzy clustering;possibilistic fuzzy clustering, feature-reduction