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.
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Ahmadi, P., Gholampour, I. & Tabandeh, M. Cluster-based sparse topical coding for topic mining and document clustering. Adv Data Anal Classif 12, 537–558 (2018). https://doi.org/10.1007/s11634-017-0280-3
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DOI: https://doi.org/10.1007/s11634-017-0280-3