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http://dx.doi.org/10.6109/jkiice.2013.17.2.347

Enhancing Document Clustering Using Term Re-weighting Based on Semantic Features  

Park, Sun (목포대학교)
Kim, Kyungjun (대구디지털산업진흥원)
Kim, Kyung Ho (목포대학교)
Lee, Seong Ro (목포대학교)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.17, no.2, 2013 , pp. 347-354 More about this Journal
Abstract
In this paper, we propose a enhancing document clustering method using term re-weighting by the expanded term. The proposed method extracts the important terms of documents in cluster using semantic features, which it can well represent the topics of document to expand term using WordNet. Besides, the method can improve the performance of document clustering using re-weighting terms based on the expanded terms. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than the normal document clustering methods.
Keywords
document clustering; semantic features; the expanded terms; the terms re-weighting;
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Times Cited By KSCI : 5  (Citation Analysis)
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