A distributed hierarchical clustering system for web mining

Catherine W. Wen; Huan Liu; Wilson X. Wen; Jeffery Zheng

doi:10.1007/3-540-47714-4_10

A distributed hierarchical clustering system for web mining

Catherine W. Wen, Huan Liu, Wilson X. Wen, Jeffery Zheng

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Scopus citations

Abstract

This paper proposes a novel method of distributed hierarchical clustering for Web mining. The method is closely related to our early work of SelfGenerated Neural Networks (SGNN), which is in turn based on both selforganizing neural network and concept formation. The complexity of the algorithm is at most O(MNlogN). With the distributed implementation the method can be easily scaled up. The method is independent of the order the web documents presented. The method produces a natural conceptual hierarchy but not a binary tree. The method can include multimedia information into the same cluster hierarchy. A visualization mechanism has been developed for the clustering method and it shows the cluster hierarchy generated by the method has very high quality. The clustering process is fully automatic, and no human intervention is required. A clustering system has been built based on the proposed method, which can be used to automatically generate multimedia search engines, web directories, decision-making assistance systems, knowledge management systems, and personalized knowledge portals.

Original language	English (US)
Title of host publication	Advances in Web-Age Information Management - 2nd International Conference, WAIM 2001, Proceedings
Editors	X. Sean Wang, Ge Yu, Hongjun Lu
Publisher	Springer Verlag
Pages	103-113
Number of pages	11
ISBN (Print)	9783540477143
DOIs	https://doi.org/10.1007/3-540-47714-4_10
State	Published - 2001
Event	2nd International Conference on Web-Age Information Management, WAIM 2001 - Xi’an, China Duration: Jul 9 2001 → Jul 11 2001

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2118
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2nd International Conference on Web-Age Information Management, WAIM 2001
Country/Territory	China
City	Xi’an
Period	7/9/01 → 7/11/01

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/3-540-47714-4_10

Cite this

Wen, C. W., Liu, H., Wen, W. X., & Zheng, J. (2001). A distributed hierarchical clustering system for web mining. In X. S. Wang, G. Yu, & H. Lu (Eds.), Advances in Web-Age Information Management - 2nd International Conference, WAIM 2001, Proceedings (pp. 103-113). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2118). Springer Verlag. https://doi.org/10.1007/3-540-47714-4_10

A distributed hierarchical clustering system for web mining. / Wen, Catherine W.; Liu, Huan; Wen, Wilson X. et al.
Advances in Web-Age Information Management - 2nd International Conference, WAIM 2001, Proceedings. ed. / X. Sean Wang; Ge Yu; Hongjun Lu. Springer Verlag, 2001. p. 103-113 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2118).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wen, CW, Liu, H, Wen, WX & Zheng, J 2001, A distributed hierarchical clustering system for web mining. in XS Wang, G Yu & H Lu (eds), Advances in Web-Age Information Management - 2nd International Conference, WAIM 2001, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2118, Springer Verlag, pp. 103-113, 2nd International Conference on Web-Age Information Management, WAIM 2001, Xi’an, China, 7/9/01. https://doi.org/10.1007/3-540-47714-4_10

Wen CW, Liu H, Wen WX, Zheng J. A distributed hierarchical clustering system for web mining. In Wang XS, Yu G, Lu H, editors, Advances in Web-Age Information Management - 2nd International Conference, WAIM 2001, Proceedings. Springer Verlag. 2001. p. 103-113. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-47714-4_10

Wen, Catherine W. ; Liu, Huan ; Wen, Wilson X. et al. / A distributed hierarchical clustering system for web mining. Advances in Web-Age Information Management - 2nd International Conference, WAIM 2001, Proceedings. editor / X. Sean Wang ; Ge Yu ; Hongjun Lu. Springer Verlag, 2001. pp. 103-113 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper proposes a novel method of distributed hierarchical clustering for Web mining. The method is closely related to our early work of SelfGenerated Neural Networks (SGNN), which is in turn based on both selforganizing neural network and concept formation. The complexity of the algorithm is at most O(MNlogN). With the distributed implementation the method can be easily scaled up. The method is independent of the order the web documents presented. The method produces a natural conceptual hierarchy but not a binary tree. The method can include multimedia information into the same cluster hierarchy. A visualization mechanism has been developed for the clustering method and it shows the cluster hierarchy generated by the method has very high quality. The clustering process is fully automatic, and no human intervention is required. A clustering system has been built based on the proposed method, which can be used to automatically generate multimedia search engines, web directories, decision-making assistance systems, knowledge management systems, and personalized knowledge portals.",

author = "Wen, {Catherine W.} and Huan Liu and Wen, {Wilson X.} and Jeffery Zheng",

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AB - This paper proposes a novel method of distributed hierarchical clustering for Web mining. The method is closely related to our early work of SelfGenerated Neural Networks (SGNN), which is in turn based on both selforganizing neural network and concept formation. The complexity of the algorithm is at most O(MNlogN). With the distributed implementation the method can be easily scaled up. The method is independent of the order the web documents presented. The method produces a natural conceptual hierarchy but not a binary tree. The method can include multimedia information into the same cluster hierarchy. A visualization mechanism has been developed for the clustering method and it shows the cluster hierarchy generated by the method has very high quality. The clustering process is fully automatic, and no human intervention is required. A clustering system has been built based on the proposed method, which can be used to automatically generate multimedia search engines, web directories, decision-making assistance systems, knowledge management systems, and personalized knowledge portals.

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A distributed hierarchical clustering system for web mining

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