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International Joint Conference on Rough Sets

IJCRS 2017: Rough Sets pp 313–325Cite as

A Semi-supervised Three-Way Clustering Framework for Multi-view Data

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

Abstract

A new semi-supervised clustering framework for uncertain multi-view data is proposed inspired by the theory of three-way decisions, which is an alternative formulation different from the ones used in the existing studies. A cluster is represented by three regions such as the core region, fringe region and trivial region. The three-way representation intuitively shows which objects are fringe to the cluster. The proposed method is an iterative processing which includes two parts: (1) the three-way spectral clustering algorithm which is devised to obtain the three-way representation result; and (2) the active learning strategy which is designed to obtain the prior supervision information from the fringe regions, and the pairwise constraints information is used to adjust the similarity matrix between objects. Experimental results show that the proposed method can cluster multi-view data effectively and is better in performances than the compared single-view clusterings and other semi-supervised clustering approaches.

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Notes

  1. 1.

    https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html.

  2. 2.

    https://archive.ics.uci.edu/ml/datasets.html.

  3. 3.

    http://linqs.umiacs.umd.edu/projects//projects/lbc/index.html.

  4. 4.

    http://linqs.umiacs.umd.edu/projects//projects/lbc/index.html.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61379114 & 61533020.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People’s Republic of China

    Hong Yu, Xincheng Wang & Guoyin Wang

Authors
  1. Hong Yu
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  2. Xincheng Wang
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  3. Guoyin Wang
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Corresponding author

Correspondence to Hong Yu .

Editor information

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., Wang, X., Wang, G. (2017). A Semi-supervised Three-Way Clustering Framework for Multi-view Data. 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_23

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  • DOI: https://doi.org/10.1007/978-3-319-60840-2_23

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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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