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Key-feature-based clustering algorithm for search engine results
Zhang Hui, Xie Ke, Pang Bin, Wu Hui*
National Laboratory of Software Development Environment, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

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Abstract�� To solve the problem that users of web search engines are often forced to sift through the long ordered list of document, a new key-feature clustering (KFC) algorithm was presented to help locate the valuable search results that the users really needed, which was different from VSM. The algorithm firstly extracted some key features from the keywords in the search results. Then the relationships between key features were analyzed and features were clustered. Finally, the documents were clustered based on these clusters of key features. The algorithm was tested and validated by the results of experiments.
Keywords�� search engines   algorithm   feature extraction   document clustering   vector space model   KFC algorithm     
Received 2006-08-23;
Fund:

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About author: �� ��(1968-),��,�㽭�Ͱ���,������, hzhang@nlsde.buaa.edu.cn.
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�Ż�, л��, �ӱ�, ���.һ�ֻ��ڹؼ����������������������㷨[J]  �������պ����ѧѧ��, 2007,V33(06): 739-742
Zhang Hui, Xie Ke, Pang Bin, Wu Hui.Key-feature-based clustering algorithm for search engine results[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2007,V33(06): 739-742
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http://bhxb.buaa.edu.cn//CN/     ��     http://bhxb.buaa.edu.cn//CN/Y2007/V33/I06/739
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