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Soft Computing

Erschienen in:

01.01.2013 | Original Paper

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

verfasst von: Yukihiro Hamasuna, Yasunori Endo

Erschienen in: Soft Computing | Ausgabe 1/2013

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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.

Vorheriger Artikel A new order relation on fuzzy soft sets and its application

Nächster Artikel Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks

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Titel: On semi-supervised fuzzy c-means clustering for data with clusterwise tolerance by opposite criteria
verfasst von: Yukihiro Hamasuna
Yasunori Endo
Publikationsdatum: 01.01.2013
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 1/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0904-7

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