Vague C-means clustering algorithm

  • Authors:

  • Chao Xu;Peilin Zhang;Bing Li;Dinghai Wu;Hongbo Fan

  • Affiliations:

  • Department Seven, Mechanical Engineering College, Shijiazhuang 050003, PR China;Department Seven, Mechanical Engineering College, Shijiazhuang 050003, PR China;Department Four, Mechanical Engineering College, Shijiazhuang 050003, PR China;Department Seven, Mechanical Engineering College, Shijiazhuang 050003, PR China;Department Seven, Mechanical Engineering College, Shijiazhuang 050003, PR China

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2013

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Abstract

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.