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Multi-view subspace text clustering

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Abstract

Text clustering has become an important challenge in artificial intelligence since several applications require to automatically organize documents into homogeneous topics. Given the availability of several text representation models, text documents can be organized through a multi-view text clustering approach. In this context, we propose a new subspace multi-view text clustering method (MVSTC). The proposed method offers a rich representation of text by integrating several models to detect different aspects of text such as syntactic, topic, and semantic features. MVSTC is capable of discovering latent correlations between documents by projecting the data onto a topological map. MVSTC seeks a subspace representation based on a low-rank and sparse representation to capture the global and local structure of multi-view textual data. Extensive experiments on real text data sets demonstrate that our method outperforms the existing multi-view clustering methods in terms of several evaluation metrics.

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Data Availability

The Reuters data set is available at http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html the BBC Sport dataset is available at http://mlg.ucd.ie/datasets/bbc.html the 20 Newsgroup dataset is available at https://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html The webKB dataset is available at https://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/

Notes

  1. http://www.daviddlewis.com/resources/testcollections/reuters21578/

  2. http://qwone.com/~jason/20Newsgroups/

  3. https://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/

  4. http://mlg.ucd.ie/datasets/bbc.html

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Authors and Affiliations

  1. Université de Tunis, Institut Supérieur de Gestion de Tunis, 41, Rue de la Liberté, Cité Bouchoucha 2000 Le Bardo, Tunis, Tunisie

    Maha Fraj, Mohamed Aymen Ben HajKacem & Nadia Essoussi

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  1. Maha Fraj
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  2. Mohamed Aymen Ben HajKacem
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  3. Nadia Essoussi
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Contributions

All authors contributed to the study’s conception and design. In detail: M.Fraj mainly contributed to conceptualization, methodology, coding/experimentation, and manuscript writing. M.A.B. Hajkacem and N. Essoussi were involved in conceptualization, methodology, and supervision. All authors agreed on the results and contributed to the final manuscript.

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Correspondence to Maha Fraj.

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Fraj, M., HajKacem, M.A.B. & Essoussi, N. Multi-view subspace text clustering. J Intell Inf Syst 62, 1583–1606 (2024). https://doi.org/10.1007/s10844-024-00897-2

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