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. Attached Files Name Description MIMEType Size Downloads Title A maximal frequent itemset approach for web document clustering Author(s) Zhuang, Ling Dai, Honghua Conference name Computer and Information Technology. Conference (4th : 2004 : Wuhan, China) Conference location Wuhan, China Conference dates 14-16 Sep. 2004 Title of proceedings Fourth International Conference on Computer and Information Technology : proceedings : September 14-16, 2004, Wuhan, China Editor(s) Wei, Daming Wang, Hui Peng, Zhiyong Kara, Atsushi He, Yanxiang Publication date 2004 Conference series Computer and Information Technology Conference Start page 970 End page 977 Publisher IEEE Computer Society Place of publication Los Alamitos, Calif. Summary 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 HERDC Research category E1 Full written paper - refereed Copyright notice ©2004, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30005533 Document type: Conference Paper Collection: School of Information Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 08 Dec 2008, 08:02:52 EST Fri, 18 Jun 2010, 11:11:07 EST Wed, 02 Jul 2014, 14:29:48 EST Thu, 10 Jul 2014, 13:01:12 EST Wed, 21 Oct 2015, 13:07:10 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 17 times in Scopus Search Google Scholar Access Statistics: 530 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 07 Jul 2008, 09:50:54 EST

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