Volume 5 Number 4 (Apr. 2010)
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JCP 2010 Vol.5(4): 549-556 ISSN: 1796-203X
doi: 10.4304/jcp.5.4.549-556

On-line Hot Topic Recommendation Using Tolerance Rough Set Based Topic Clustering

Yonghui Wu, Yuxin Ding, Xiaolong Wang, and Jun Xu
Intelligence Computing Research Center Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, People’s Republic of China

Abstract—In this paper we present our research of online hot topic detection and label extraction method for our hot topic recommendation system. Using a new topical feature selection method, the feature space is compressed suitable for an online system. The tolerance rough set model is used to enriching the small set of topical feature words to a topical approximation space. According to the distance defined on the topical approximation space, the web pages are clustered into groups which will be merged with document overlap. The topic labels are extracted based on the approximation topical space enriched with the useful but high frequency topical words dropped by the clustering process. The experiments show that our method could generate more information abundant classes and more topical class labels, alleviate the topical drift caused by the non-topical and noise words.

Index Terms—topic detection, tolerance rough set model, association rule, clustering, recommendation system

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Cite: Yonghui Wu, Yuxin Ding, Xiaolong Wang, and Jun Xu, " On-line Hot Topic Recommendation Using Tolerance Rough Set Based Topic Clustering," Journal of Computers vol. 5, no. 4, pp. 549-556, 2010.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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