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Multi-view K-Means Clustering with Bregman Divergences

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Artificial Intelligence (ICAI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 888))

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Abstract

Multi-view clustering has become an important task in machine learning. How to make full use of the similarities and differences among multiple views to generate clusters is a crucial issue. However, the existing multi-view clustering methods rarely consider the redundancy of the multiple views. In this paper, we propose a novel multi-view clustering method with Bregman divergences (MVBDC), where the clustering result is achieved by minimizing the Bregman divergences between clustering results obtained by weighted multiple views and the item that controls redundancy of multiple views. The experimental results on nine data sets demonstrate that our algorithm has a good clustering performance.

Supported by Chinese National Natural Science Young Foundation: Robust Clustering Models and Algorithms for Multi-source Big Data, Number: 61502289, 2016.01-2018.12.

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Authors and Affiliations

  1. The School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi Province, China

    Yan Wu, Liang Du & Honghong Cheng

  2. Institute of Big Data Science and Industry, Shanxi University, Taiyuan, 030006, Shanxi Province, China

    Yan Wu, Liang Du & Honghong Cheng

Authors
  1. Yan Wu
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  2. Liang Du
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  3. Honghong Cheng
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Corresponding authors

Correspondence to Yan Wu or Liang Du .

Editor information

Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  2. Hong Kong University of Science and Technology, Hong Kong SAR, China

    Qiang Yang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. JD Finance, Beijing, China

    Yu Zheng

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Wu, Y., Du, L., Cheng, H. (2018). Multi-view K-Means Clustering with Bregman Divergences. In: Zhou, ZH., Yang, Q., Gao, Y., Zheng, Y. (eds) Artificial Intelligence. ICAI 2018. Communications in Computer and Information Science, vol 888. Springer, Singapore. https://doi.org/10.1007/978-981-13-2122-1_3

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  • DOI: https://doi.org/10.1007/978-981-13-2122-1_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2121-4

  • Online ISBN: 978-981-13-2122-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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