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A Comparative Study on Utilization of Semantic Information in Fuzzy Co-clustering

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13199))

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Abstract

Fuzzy co-clustering is a technique for extracting co-clusters of mutually familiar pairs of objects and items from co-occurrence information among them, and has been utilized in document analysis on document-keyword relations and market analysis on purchase preferences of customers with products. Recently, multi-view data clustering attracts much attentions with the goal of revealing the intrinsic features among multi-source data stored over different organizations. In this paper, three-mode document data analysis is considered under multi-view analysis of document-keyword relations in conjunction with semantic information among keywords, where the results of two different approaches are compared. Fuzzy Bag-of-Words (Fuzzy BoW) introduces semantic information among keywords such that co-occurrence degrees are counted supported by fuzzy mapping of semantically similar keywords. On the other hand, three-mode fuzzy co-clustering simultaneously considers the cluster-wise aggregation degree among documents, keywords and semantic similarities. Numerical results with a Japanese novel document demonstrate the different features of these two approaches.

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Notes

  1. 1.

    English translation is also available in Eldritch Press (http://www.ibiblio.org/eldritch/).

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Acknowledgment

This work was supported in part by JSPS KAKENHI Grant Number JP18K11474.

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Authors and Affiliations

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

    Yusuke Takahata, Katsuhiro Honda & Seiki Ubukata

Authors
  1. Yusuke Takahata
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  2. Katsuhiro Honda
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  3. Seiki Ubukata
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Corresponding author

Correspondence to Katsuhiro Honda .

Editor information

Editors and Affiliations

  1. Osaka Prefecture University, Sakai, Osaka, Japan

    Katsuhiro Honda

  2. University of Hyogo, Kobe, Japan

    Tomoe Entani

  3. Osaka Prefecture University, Sakai, Japan

    Seiki Ubukata

  4. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Van-Nam Huynh

  5. Osaka University, Toyonaka, Osaka, Japan

    Masahiro Inuiguchi

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Takahata, Y., Honda, K., Ubukata, S. (2022). A Comparative Study on Utilization of Semantic Information in Fuzzy Co-clustering. In: Honda, K., Entani, T., Ubukata, S., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2022. Lecture Notes in Computer Science(), vol 13199. Springer, Cham. https://doi.org/10.1007/978-3-030-98018-4_18

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  • DOI: https://doi.org/10.1007/978-3-030-98018-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98017-7

  • Online ISBN: 978-3-030-98018-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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