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International Symposium on Methodologies for Intelligent Systems

ISMIS 2018: Foundations of Intelligent Systems pp 389–398Cite as

Three-Way Spectral Clustering

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11177))

Abstract

In recent years, three-way clustering has shown promising performance in many different fields. In this paper, we present a new three-way spectral clustering by combining three-way decision and spectral clustering. In the proposed algorithm, we revise the process of spectral clustering and obtain an upper bound of each cluster. Perturbation analysis is applied to separate the core region from upper bound and the differences between upper bound and core region are regarded as the fringe region of specific cluster. The results on UCI data sets show that such strategy is effective in reducing the value of DBI and increasing the values of ACC and AS.

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Author information

Authors and Affiliations

  1. School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, China

    Hong Shi, Qiang Liu & Pingxin Wang

  2. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, 050024, China

    Pingxin Wang

Authors
  1. Hong Shi
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  2. Qiang Liu
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  3. Pingxin Wang
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Corresponding author

Correspondence to Pingxin Wang .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. American University, Washington, DC, USA

    Nathalie Japkowicz

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Jiming Liu

  4. University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

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Shi, H., Liu, Q., Wang, P. (2018). Three-Way Spectral Clustering. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_37

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  • DOI: https://doi.org/10.1007/978-3-030-01851-1_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01850-4

  • Online ISBN: 978-3-030-01851-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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