A distributed hierarchical clustering system for web mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2118, Issue , 2001, Pages 103-113

  Wen, Catherine W ^a   Liu, Huan ^a   Wen, Wilson X ^b   Zheng, Jeffery ^c  

^a Arizona State University   (United States)

^b GoConnect   (Australia)

^c CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY TREES; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATA MINING; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE MANAGEMENT; PORTALS; SEARCH ENGINES;

CLUSTERING PROCESS; CONCEPTUAL HIERARCHY; DISTRIBUTED IMPLEMENTATION; HIER-ARCHICAL CLUSTERING; HUMAN INTERVENTION; KNOWLEDGE MANAGEMENT SYSTEM; MULTIMEDIA INFORMATION; SELF-ORGANIZING NEURAL NETWORK;

DECISION MAKING;

EID: 84974662382     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47714-4_10     Document Type: Conference Paper

References (19)

1. M. R. Anderberg, Cluster Analysis for Applications, New Yourk, Academic, 1973
2. R. Baeza-Yates and B. Ribeiro-Neto, Moden Information Retrieval, Addison Wesley, ACM Press, 1999
3. T. Caeli, L. Guan, and W. Wen, Modularity in Neural Computing, Invited paper, Proceedings of IEEE, Vol 87, No. 9, 1999
4. D. Fisher, Knowledge Acquisition via Incremental Conceptual Clustering, Machine Learning, vol 2, 1987
5. D. Gibson, J. Kleinberg, and P. Raghavan. Inferring Web Communities from Link Topolgy. In Conference on hypertext and Hypermedia. ACM, 1998
6. D. Gordon, A review of hierarchical classification, J. Royal Statistical Society Series A, 150(2), 119-137, 1987
7. D.O. Hebb, The Organization of Behaviour, New York, Willy
8. Iona, OrbixWeb Programmer’s Guide, Sept. 1998
9. T. Kohonen, Self-Organization and Associative Memory, Springer-Verlag, Berlin, 1984
10. R. Kosala and H. Blockeel. Web Mining Research: A Survey. SIKDD Explorations, Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining. June 2 2000, Volume 2, Issue 1. pp 1-15
11. Natsev, R. Rastogi, and K. Shim. Walrus: A Similarity Retrieva Algorithm for Image Databases. In Proc. 1999 ACM-SIGMOD Conf. On Management of Data (SIGMOD’99), pp 395-406
12. S. Salton, The Smart retrieval system, Englewood cliffs, N.J., Prentice Hall. 1971
13. J. Srivastava, R. Cooley, M. Deshpande, and P-N. Tan. Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIKDD Explorations, Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining. January 2000, Volume 1, Issue 2. pp 12-23
14. W. Wen, SGNNN: Self-Generating Network of Neural Networks, invited paper, Australian Conference on Neural Networks, Brisbane, 1998
15. W. Wen, A. Jennings, and H. Liu, Self-Generating Neural Networks, International Joint Conference on Neural Networks, Baltimore, 1992
16. L. Wang, On Competitive learning, IEEE Transactions on Neural Networks, vol. 8, no. 5, pp. 1214-1217, Sept. 1997
17. O.R. Zaiane, J. Han, and H. Zhu. Mining Recurrent Items in Multimedia with Progressive Resolution Refinement. In Proc. 2000 Int. Conf. Data Engineering (ICDE’00), pp 195-209
18. Z.J. Zheng and C.H.C. Leung, Graph Indexes of 2D- Thinned Images for Rapid Content- based Image Retrieval, Journal of Visual Communication and Image Representation, Vol. 8, No. 2, pp. 121-134, 1997
19. Z.J. Zheng and C.H.C. Leung, Automatic Image Indexing for Rapid Content-based Retrieval, in Proceedings of International Workshop on Multi-media Database Management Systems, IEEE Computer Society Press, 1996.