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ACMSE
2007
ACM

Enhancing clustering blog documents by utilizing author/reader comments

9 years 5 months ago
Enhancing clustering blog documents by utilizing author/reader comments
Blogs are a new form of internet phenomenon and a vast everincreasing information resource. Mining blog files for information is a very new research direction in data mining. We propose to include the title, body, and comments of the blog pages in clustering datasets from blog documents. In particular, we argue that the author/reader comments of the blog pages may have more discriminating effect in clustering blog documents. We constructed a word-page matrix by downloading blog pages from a well-known website and experimented a k-means clustering algorithm with different weights assigned to the title, body, and comment parts. Our experimental results show that assigning a larger weight value to the blog comments helps the k-means algorithm produce better clustering solutions. The experimental results confirm our hypothesis that the author/reader comments of the blog files are very useful in discriminating blog files. Categories and Subject Descriptors H.3.3. [Information Search and Re...
Beibei Li, Shuting Xu, Jun Zhang
Added 12 Aug 2010
Updated 12 Aug 2010
Type Conference
Year 2007
Where ACMSE
Authors Beibei Li, Shuting Xu, Jun Zhang
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