Vague C-means clustering algorithm

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• 作者：Chao Xu^a ; ^{xuchao198602@163.com} ; Peilin Zhang^a ; Bing Li^b ; Dinghai Wu^a ; Hongbo Fan^a
• 关键词：Fuzzy clustering ; Vague set (VS) ; Quantum-behaved particle swarm optimization (QPSO)
• 刊名：Pattern Recognition Letters
• 出版年：2013
• 出版时间：1 April, 2013
• 年：2013
• 卷：34
• 期：5
• 页码：505-510
• 全文大小：561 K

文摘

A set of objectives are partitioned into groups by means of fuzzy set theory-based clustering approaches, which ignores the hesitancy introduced by the relationship degree between two entities. The interval-based membership generalization in vague sets (VSs) is more expressive than fuzzy sets (FSs) in describing and dealing with data vagueness. In this paper, we introduce a fuzzy clustering algorithm in the context of VSs theory and fuzzy C-means clustering (FCM), i.e., Vague C-means clustering algorithm (VCM). First, the objective function of VCM and the definition of interval-based membership function are given. Then, the QPSO (quantum-behaved particle swarm optimization)-based VCM calculation is proposed. Contrastive experimental results show that the proposed scheme is more effective and more efficient than FCM and three varieties of FCM, that is, FCM-HDGA, GK-FCM and KL-FCM. Besides, the paper also discusses the influence of the VCM parameters on the clustering results.