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A Framework of Three-Way Cluster Analysis

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Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10314))

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Abstract

A new framework of clustering is proposed inspired by the theory of three-way decisions, which is an alternative formulation different from the ones used in the existing studies. The novel three-way representation intuitively shows which objects are fringe to the cluster and it is proposed for dealing with uncertainty clustering. Instead of using two regions to represent a cluster by a single set, a cluster is represented using three regions through a pair of sets, and there are three regions such as the core region, fringe region and trivial region. A cluster is therefore more realistically characterized by a set of core objects and a set of boundary objects. In this paper, we also illustrate an algorithm for incomplete data by using the proposed evaluation-based three-way cluster model. The preliminary experimental results show that the proposed method is effective for clustering incomplete data which is one kind of uncertainty data. Furthermore, this paper reviews some three-way clustering approaches and discusses some future perspectives and potential research topics based on the three-way cluster analysis.

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Acknowledgments

I am grateful to Professor Yiyu Yao for his suggestions, and I would like to thank Ms. Ting Su for her help to complete the experimental work. In addition, this work was supported in part by the National Natural Science Foundation of China under grant No. 61379114 and No. 61533020.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Hong Yu

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  1. Hong Yu
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Correspondence to Hong Yu .

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Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Yu, H. (2017). A Framework of Three-Way Cluster Analysis. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10314. Springer, Cham. https://doi.org/10.1007/978-3-319-60840-2_22

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  • DOI: https://doi.org/10.1007/978-3-319-60840-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60839-6

  • Online ISBN: 978-3-319-60840-2

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