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Weighted multi-view co-clustering (WMVCC) for sparse data

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Abstract

Multi-view clustering has gained importance in recent times due to the large-scale generation of data, often from multiple sources. Multi-view clustering refers to clustering a set of objects which are expressed by multiple set of features, known as views, such as movies being expressed by the list of actors or by a textual summary of its plot. Co-clustering, on the other hand, refers to the simultaneous grouping of data samples and features under the assumption that samples exhibit a pattern only under a subset of features. This paper combines multi-view clustering with co-clustering and proposes a new Weighted Multi-View Co-Clustering (WMVCC) algorithm. The motivation behind the approach is to use the diversity of features provided by multiple sources of information while exploiting the power of co-clustering. The proposed method expands the clustering objective function to a unified co-clustering objective function across all the multiple views. The algorithm follows the k-means strategy and iteratively optimizes the clustering by updating cluster labels, features, and view weights. A local search is also employed to optimize the clustering result using weighted multi-step paths in a graph. Experiments are conducted on several benchmark datasets. The results show that the proposed approach converges quickly, and the clustering performance significantly outperforms other recent and state-of-the-art algorithms on sparse datasets.

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Data availability

Publicly available data (references mentioned in text).

Notes

  1. Links to datasets used are available here: https://sites.google.com/site/fawadsyed/datasets

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Acknowledgements

Khadija Khan would like to thank the Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan for providing her a fully funded scholarship to pursue the MS degree under its GA-1 scheme.

Code availability

Shall be released in future.

Funding

This work is part of a graduate thesis (Ms. Khadija Khan) funded by the Ghulam Ishaq Khan Institute under its scholarship (GA-1) scheme.

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Authors and Affiliations

  1. Machine Learning and Data Science (MDS) Lab, G.I.K Institute, Topi, KPK, 23460, Pakistan

    Syed Fawad Hussain, Khadija Khan & Rashad Jillani

  2. Faculty of Computer Science and Engineering, G.I.K. Institute, Topi, KPK, 23460, Pakistan

    Syed Fawad Hussain, Khadija Khan & Rashad Jillani

Authors
  1. Syed Fawad Hussain
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  2. Khadija Khan
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  3. Rashad Jillani
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Contributions

Syed Fawad Hussain is credited with the conceptualization of the idea, editing, and writing portions (method description and analysis of results) of the text; Khadija Khan wrote the code including those of some of the competing methods and running the simulations; Rashad Jilani helped with writing a substantial part of the manuscript and generating the graphs.

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Correspondence to Syed Fawad Hussain.

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Hussain, S.F., Khan, K. & Jillani, R. Weighted multi-view co-clustering (WMVCC) for sparse data. Appl Intell 52, 398–416 (2022). https://doi.org/10.1007/s10489-021-02405-3

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