Vague C-means clustering algorithm

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.

► Vague C-means clustering algorithm (VCM) is proposed. ► We define the novel objective function of VCM. ► We give the definition of interval-based membership. ► We give the QPSO-based solution of VCM.