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

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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.

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Authors and Affiliations

  1. Department of Informatics, School of Science and Engineering, Kinki University, 3-4-1 Kowakae, Higashiōsaka, Osaka, 577-8502, Japan

    Yukihiro Hamasuna

  2. Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan

    Yasunori Endo

  3. International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, 2361, Laxenburg, Austria

    Yasunori Endo

Authors
  1. Yukihiro Hamasuna
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  2. Yasunori Endo
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Correspondence to Yukihiro Hamasuna.

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Hamasuna, Y., Endo, Y. On semi-supervised fuzzy c-means clustering for data with clusterwise tolerance by opposite criteria. Soft Comput 17, 71–81 (2013). https://doi.org/10.1007/s00500-012-0904-7

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  • DOI: https://doi.org/10.1007/s00500-012-0904-7

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