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Information Technology Journal

Year: 2009 | Volume: 8 | Issue: 4 | Page No.: 453-464
Research Article

A Tolerance Rough Set Based Semantic Clustering Method for Web Search Results

Xian-Jun Meng
Intelligence Computing Research Center, Harbin Institute of Technology, Shenzhen Graduate School, 518055, People`s Republic of China
Qing-Cai Chen
Intelligence Computing Research Center, Harbin Institute of Technology, Shenzhen Graduate School, 518055, People`s Republic of China
Xiao-Long Wang
Intelligence Computing Research Center, Harbin Institute of Technology, Shenzhen Graduate School, 518055, People`s Republic of China
The objective of this study is to present a new web search results clustering algorithm which uses the tolerance rough set based approach to find the different meanings of the query in web search results and then organizes these results into different clusters according to their related meanings about query. Each meaning of the query can be represented by its contexts in each result and if there is a significant correlation between two context words, it is more likely that these two words represent the same meaning of query and also suitable as good indication of the meaning of query. In this study, the search results are organized in groups that each group of results relates to context words with high correlations and then these groups are merged into the final clusters representation using both cluster contents similarity and cluster documents overlap. The correlated context words with high documents coverage are selected as the labels of each cluster. Some experiments were conducted on different search results sets based on various queries. The results and comparisons of the proposed algorithm with that of the popular search results clustering algorithms through an empirical evaluation establish the viability of this proposed approach.
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