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Enhancing Document Clustering Method using Synonym of Cluster Topic and Similarity  

Park, Sun (Institute of Information Science and Engineering Research, Mokpo National University)
Kim, Kyung-Jun (Department of Computer Science, KAIST)
Lee, Jin-Seok (NIPA)
Lee, Seong-Ro (Department of Information and Electronics)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.48, no.5, 2011 , pp. 30-38 More about this Journal
Abstract
This paper proposes a new enhancing document clustering method using a synonym of cluster topic and the similarity. The proposed method can well represent the inherent structure of document cluster set by means of selecting terms of cluster topic based on the semantic features by NMF. It can solve the problem of "bags of words" by using of expanding the terms of cluster topics which uses the synonyms of WordNet. Also, it can improve the quality of document clustering which uses the cosine similarity between the expanded cluster topic terms and document set to well cluster document with respect to the appropriation cluster. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.
Keywords
document clustering; NMF, non-negative matrix factorization; semantic features; synonym; cosine similarity;
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Times Cited By KSCI : 3  (Citation Analysis)
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