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Exclusive Item Partition with Fuzziness Tuning in MMMs-Induced Fuzzy Co-clustering

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9978))

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Abstract

Fuzzy co-clustering achieves dual partition of object-item pairs by estimating fuzzy memberships of them. In the multinomial mixtures-induced model, object memberships present the exclusive assignment to clusters while item memberships describe relative typicality in each cluster. In order to improve the interpretability of item partition, exclusive penalty was adopted for item memberships in previous works, where item fuzzy memberships are estimated reflecting both fuzzification penalty and exclusive penalty. In this paper, the characteristics of exclusive item penalty are further studied considering the influences of the item fuzziness weight with different fuzziness degrees.

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Acknowledgment

This work was supported in part by JSPS KAKENHI Grant Number JP26330281.

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Authors and Affiliations

  1. Osaka Prefecture University, Sakai, Osaka, 599-8531, Japan

    Takaya Nakano, Katsuhiro Honda, Seiki Ubukata & Akira Notsu

Authors
  1. Takaya Nakano
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  2. Katsuhiro Honda
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  3. Seiki Ubukata
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  4. Akira Notsu
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Corresponding author

Correspondence to Katsuhiro Honda .

Editor information

Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Van-Nam Huynh

  2. Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, Japan

    Masahiro Inuiguchi

  3. University of Science, Ho Chi Minh City, Vietnam

    Bac Le

  4. Duy Tan University, Da Nang, Vietnam

    Bao Nguyen Le

  5. Université de Technologie de Compiègne , Compiègne, France

    Thierry Denoeux

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Nakano, T., Honda, K., Ubukata, S., Notsu, A. (2016). Exclusive Item Partition with Fuzziness Tuning in MMMs-Induced Fuzzy Co-clustering. In: Huynh, VN., Inuiguchi, M., Le, B., Le, B., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2016. Lecture Notes in Computer Science(), vol 9978. Springer, Cham. https://doi.org/10.1007/978-3-319-49046-5_16

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  • DOI: https://doi.org/10.1007/978-3-319-49046-5_16

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  • Online ISBN: 978-3-319-49046-5

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