Blogs are a new form of internet phenomenon and a vast ever-increasing 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.

ategories and Subject Descriptors H.3.3. [Information Search and Retrieval]: Clustering, retrieval models, search process.

The research work of J. Zhang was supported in part by the National Science Foundation under grants CCR-0092532 and CCF-0527967, in part by the Kentucky Science and Engineering Foundation under grant KSEF-148-502-05-132, and in part by Alzheimer.s Association under grant NIRG-06-25460.