On semi-supervised fuzzy c-means clustering for data with clusterwise tolerance by opposite criteria

Hamasuna, Y. & Endo, Y. (2013). On semi-supervised fuzzy c-means clustering for data with clusterwise tolerance by opposite criteria. Soft Computing 17 (1) 71-81. 10.1007/s00500-012-0904-7.

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Abstract

This paper presents a new semi-supervised fuzzy c-means clustering for data with clusterwise tolerance by opposite criteria. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link, are frequently used in order to improve clustering performances. From the viewpoint of handling pairwise constraints, a new semi-supervised fuzzy c-means clustering is proposed by introducing clusterwise tolerance-based pairwise constraints. First, a concept of clusterwise tolerance-based pairwise constraints is introduced. Second, the optimization problems of the proposed method are formulated. Especially, must-link and cannot-link are handled by opposite criteria in our proposed method. Third, a new clustering algorithm is constructed based on the above discussions. Finally, the effectiveness of the proposed algorithm is verified through numerical examples.

Item Type:	Article
Uncontrolled Keywords:	Clusterwise tolerance; Fuzzy c-means clustering; Pairwise constraints; Semi-supervised clustering
Research Programs:	Advanced Systems Analysis (ASA)
Bibliographic Reference:	Soft Computing; 17(1):71-81 (January 2013) (Published online 9 August 2012)
Depositing User:	IIASA Import
Date Deposited:	15 Jan 2016 08:49
Last Modified:	27 Aug 2021 17:39
URI:	https://pure.iiasa.ac.at/10541

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