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Weighted Multi-view Clustering Based on Internal Evaluation

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MultiMedia Modeling (MMM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13834))

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Abstract

As real-world data are often represented by multiple sets of features in different views, it is desirable to improve clustering results with respect to ordinary single-view clustering by making use of the consensus and complementarity among different views. For this purpose, weighted multi-view clustering is proposed to combine multiple individual views into one single combined view, which is used to generate the final clustering result. In this paper we present a simple yet effective weighted multi-view clustering algorithm based on internal evaluation of clustering results. Observing that an internal evaluation criterion can be used to estimate the quality of clustering results, we propose to weight different views to maximize the clustering quality in the combined view. We firstly introduce an implementation of the Dunn index and a heuristic method to determine the scale parameter in spectral clustering. Then an adaptive weight initialization and updating method is proposed to improve the clustering results iteratively. Finally we do spectral clustering in the combined view to generate the clustering result. In experiments with several publicly available image and text datasets, our algorithm compares favorably or comparably with some other algorithms.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant No. 62176057 and No. 61972090

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Authors and Affiliations

  1. School of Computer Science and Technology, Dongguan University of Technology, Dongguan, 523808, China

    Haoqi Xu, Jian Hou & Huaqiang Yuan

Authors
  1. Haoqi Xu
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  2. Jian Hou
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  3. Huaqiang Yuan
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Corresponding author

Correspondence to Jian Hou .

Editor information

Editors and Affiliations

  1. University of Bergen, Bergen, Norway

    Duc-Tien Dang-Nguyen

  2. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  3. Radboud University Nijmegen, Nijmegen, The Netherlands

    Martha Larson

  4. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  5. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  6. National Institute of Information and Communications Technology, Tokyo, Japan

    Minh-Son Dao

  7. Department of Information Science and Media Studies, University of Bergen, Bergen, Norway

    Christoph Trattner

  8. La Trobe University, Melbourne, VIC, Australia

    Phoebe Chen

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Xu, H., Hou, J., Yuan, H. (2023). Weighted Multi-view Clustering Based on Internal Evaluation. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_18

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  • DOI: https://doi.org/10.1007/978-3-031-27818-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27817-4

  • Online ISBN: 978-3-031-27818-1

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