Presentation | 2007-07-03 Dimensionality Reduction via Latent Dirichlet Allocation for Document Clustering Tomonari MASADA, Senya KIYASU, Sueharu MIYAHARA, |
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Abstract(in English) | In this paper, we employ the latent Dirichlet allocation as a method for the dimensionality reduction of feature vectors and reveal its effectiveness in document clustering. In the evaluation experiment, we perform clustering on the document sets of Japanese and Korean Web news articles. We regard the categories assigned to each article as the ground truth of clustering evaluation. We compare the clustering results obtained by using the feature vectors whose entries are term frequencies with the results obtained by using the feature vectors whose dimensions are reduced by the latent Dirichlet allocation. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | document clustering / dimensionality reduction / latent Dirichlet allocation |
Paper # | DE2007-85 |
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Committee | DE |
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Conference Date | 2007/6/25(1days) |
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Registration To | Data Engineering (DE) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Dimensionality Reduction via Latent Dirichlet Allocation for Document Clustering |
Sub Title (in English) | |
Keyword(1) | document clustering |
Keyword(2) | dimensionality reduction |
Keyword(3) | latent Dirichlet allocation |
1st Author's Name | Tomonari MASADA |
1st Author's Affiliation | Faculty of Engineering, Nagasaki University() |
2nd Author's Name | Senya KIYASU |
2nd Author's Affiliation | Faculty of Engineering, Nagasaki University |
3rd Author's Name | Sueharu MIYAHARA |
3rd Author's Affiliation | Faculty of Engineering, Nagasaki University |
Date | 2007-07-03 |
Paper # | DE2007-85 |
Volume (vol) | vol.107 |
Number (no) | 131 |
Page | pp.pp.- |
#Pages | 6 |
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