Vague C-means clustering algorithm

Chao Xu

doi:10.1016/j.patrec.2012.12.001

Vague C-means clustering algorithm

2013-04-01

13

PRE

AI

DOI：10.1016/j.patrec.2012.12.001

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Abstract

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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. (C) 2012 Elsevier B.V. All rights reserved.

Keywords:

Fuzzy clustering

Vague set (VS)

Quantum-behaved particle swarm

optimization (QPSO)

Vague C-means clustering algorithm

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