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Cluster-based sparse topical coding for topic mining and document clustering

Author

Listed:
  • Parvin Ahmadi

    (Sharif University of Technology)

  • Iman Gholampour

    (Sharif University of Technology)

  • Mahmoud Tabandeh

    (Sharif University of Technology)

Abstract

In this paper, we introduce a document clustering method based on Sparse Topical Coding, called Cluster-based Sparse Topical Coding. Topic modeling is capable of improving textual document clustering by describing documents via bag-of-words models and projecting them into a topic space. The latent semantic descriptions derived by the topic model can be utilized as features in a clustering process. In our proposed method, document clustering and topic modeling are integrated in a unified framework in order to achieve the highest performance. This framework includes Sparse Topical Coding, which is responsible for topic mining, and K-means that discovers the latent clusters in documents collection. Experimental results on widely-used datasets show that our proposed method significantly outperforms the traditional and other topic model based clustering methods. Our method achieves from 4 to 39% improvement in clustering accuracy and from 2% to more than 44% improvement in normalized mutual information.

Suggested Citation

  • Parvin Ahmadi & Iman Gholampour & Mahmoud Tabandeh, 2018. "Cluster-based sparse topical coding for topic mining and document clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 537-558, September.
    • Handle: RePEc:spr:advdac:v:12:y:2018:i:3:d:10.1007_s11634-017-0280-3
    DOI: 10.1007/s11634-017-0280-3
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    References listed on IDEAS

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    1. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
    2. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
    3. Jean-Charles Lamirel, 2012. "A new approach for automatizing the analysis of research topics dynamics: application to optoelectronics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(1), pages 151-166, October.
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    Cited by:

    1. Karina Gibert & Yaroslav Hernandez-Potiomkin, 2023. "A Unified Formal Framework for Factorial and Probabilistic Topic Modelling," Mathematics, MDPI, vol. 11(20), pages 1-27, October.

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