An Efficient Algorithm for Clustering Search Engine Results

Authors:
Hui Zhang;Bin Pang;Ke Xie;Hui Wu
Affiliations:
National Laboratory of Software Development Environment, Beihang University, Beijing 100083, China;National Laboratory of Software Development Environment, Beihang University, Beijing 100083, China;National Laboratory of Software Development Environment, Beihang University, Beijing 100083, China;National Laboratory of Software Development Environment, Beihang University, Beijing 100083, China
Venue:
Computational Intelligence and Security
Year:
2007

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SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data

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Abstract

With the increasing number of Web documents in the Internet, the most popular keyword-matching-based search engines, such as Google, often return a long list of search results ranked based on their relevancy and importance to the query. To cluster the search engine results can help users find the results in several clustered collections, so it is easy to locate the valuable search results that the users really needed. In this paper, we propose a new Key-Feature Clustering (KFC) algorithm which firstly extracts the significant keywords from the results as key features and cluster them, then clusters the documents based on these clustered key features. At last, the paper presents and analyzes the results from experiments we conducted to test and validate the algorithm.