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Comparing LDA with pLSI as a Dimensionality Reduction Method in Document Clustering


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タイトル: Comparing LDA with pLSI as a Dimensionality Reduction Method in Document Clustering
著者: Masada, Tomonari / Kiyasu, Senya / Miyahara, Sueharu
発行日: 2008年 3月
出版者: Springer
引用: Lecture Notes in Computer Science, 4938, pp.13-26; 2008
抄録: In this paper, we compare latent Dirichlet allocation (LDA) with probabilistic latent semantic indexing (pLSI) as a dimensionality reduction method and investigate their effectiveness in document clustering by using real-world document sets. For clustering of documents, we use a method based on multinomial mixture, which is known as an efficient framework for text mining. Clustering results are evaluated by F-measure, i.e., harmonic mean of precision and recall. We use Japanese and Korean Web articles for evaluation and regard the category assigned to each Web article as the ground truth for the evaluation of clustering results. Our experiment shows that the dimensionality reduction via LDA and pLSI results in document clusters of almost the same quality as those obtained by using original feature vectors. Therefore, we can reduce the vector dimension without degrading cluster quality. Further, both LDA and pLSI are more effective than random projection, the baseline method in our experiment. However, our experiment provides no meaningful difference between LDA and pLSI. This result suggests that LDA does not replace pLSI at least for dimensionality reduction in document clustering.
記述: The original publication is available at www.springerlink.com / Large-scale Knowledge Resources: Construction and Application - Third International Conference on Large-scale Knowledge Resources, Lkr 2008, Tokyo, Japan, March 3-5, 2008, Proceedings
URI: http://hdl.handle.net/10069/16305
ISBN: 978-3-540-78158-5
ISSN: 03029743
DOI: 10.1007/978-3-540-78159-2_2
資料タイプ: Journal Article
原稿種類: author
出現コレクション:060 学術雑誌論文

引用URI : http://hdl.handle.net/10069/16305

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