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

Parvin Ahmadi (), Iman Gholampour () and Mahmoud Tabandeh ()
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Parvin Ahmadi: Sharif University of Technology
Iman Gholampour: Sharif University of Technology
Mahmoud Tabandeh: Sharif University of Technology

Advances in Data Analysis and Classification, 2018, vol. 12, issue 3, No 5, 537-558

Abstract: 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.

Keywords: Document clustering; Topic model; Sparse topical coding; K-means; 68T50 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11634-017-0280-3

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