A maximal frequent itemset approach for web document clustering
Zhuang, Ling and Dai, Honghua 2004, A maximal frequent itemset approach for web document clustering, in Fourth International Conference on Computer and Information Technology : proceedings : September 14-16, 2004, Wuhan, China, IEEE Computer Society, Los Alamitos, Calif., pp. 970-977.
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Title
A maximal frequent itemset approach for web document clustering
To efficiently and yet accurately cluster Web documents is of great interests to Web users and is a key component of the searching accuracy of a Web search engine. To achieve this, this paper introduces a new approach for the clustering of Web documents, which is called maximal frequent itemset (MFI) approach. Iterative clustering algorithms, such as K-means and expectation-maximization (EM), are sensitive to their initial conditions. MFI approach firstly locates the center points of high density clusters precisely. These center points then are used as initial points for the K-means algorithm. Our experimental results tested on 3 Web document sets show that our MFI approach outperforms the other methods we compared in most cases, particularly in the case of large number of categories in Web document sets.
ISBN
0769522165 9780769522166
Language
eng
Field of Research
080699 Information Systems not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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