Latent Semantic Space for Web Clustering - Data Mining: Foundations and Practice - page 76

We can effectively discover such a maximal simplexes and use them to
cluster the collection of Web pages. Based on our web site and our experi-
ments, we find that LSS is a very good way to organize the unstructured and
semi-structure data into several semantic topics. It illustrates that geometric
complexes are effective models for automatic Web pages clustering.
References
1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets
of items in large databases. In Proceedings of the 1993 International Conference
on Management of Data (SIGMOD 93) , pages 207-216, May 1993
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In
Proceedings of the 20th VLDB Conference , pages 487-499, 1994
3. D. Boley, M. Gini, R. Gross, E.-H. Han, K. Hastings, G. Karypis, V. Kumar,
B. Mobasher, and J. Moore. Document categorization and query generation on
the world wide web using webace. Artificial Intelligence Review , 13(5-6):365-
391, 1999
4. S. Brin, R. Motwani, J. Ullman, and S. Tsur. Dynamic itemset counting and
implication rules for market basket data. In Proceedings of ACM SIGMOD
International Conference on Management of Data , pages 255-264, 1997
5. M. S. Chen, J. Han, and P. S. Yu. Data mining: An overview from a database
perspective. IEEE Transaction on Knowledge and Data Engineering , 8(6):866-
883, 1996
6. R. Feldman, Y. Aumann, A. Amir, W. Klosgen, and A. Zilberstien. Text mining
at the term level. In Proceedings of 3rd International Conference on Knowledge
Discovery, KDD-97 , pages 167-172, Newport Beach, CA, 1998
7. R. Feldman, I. Dagan, and W. Klosgen. E cient algorithms for mining and
manipulating associations in texts. In Cybernetics and Systems, The 13th Eu-
ropean Meeting on Cybernetics and Research , volume II, Vienna, Austria, April
1996
8. R. Feldman, M. Fresko, H. Hirsh, Y. Aumann, O. Liphstat, Y. Schler, and
M. Rajman. Knowledge management: A text mining approach. In Proceedings of
2nd International Conference on Practical Aspects of Knowledge Management ,
pages 29-30, Basel, Switzerland, 1998
9. R. Feldman and H. Hirsh. Mining associations in text in the presence of back-
ground knowledge. In Proceedings of 3rd International Conference on Knowledge
Discovery , pages 343-346, 1996
10. N. Fuhr and C. Buckley. A probabilistic learning approach for document index-
ing. ACM Transactions on Information Systems , 9(3):223-248, 1991
11. J. D. Holt and S. M. Chung. E cient mining of association rules in text data-
bases. In Proceedings of CIKM , Kansas City, MO, 1999
12. A. Joshi and Z. Jiang. Retriever: Improving web search engine results using
clustering. In A. Gangopadhyay, editor, Managing Business with Electronic
Commerce: Issues and Trends , chapter 4. World Scientific, New York, 2001
13. B. Lent, R. Agrawal, and R. Srikant. Discovering trends in text databases. In
Proceedings of 3rd International Conference on Knowledge Discovery, KDD-97 ,
pages 227-230, Newport Beach, CA, 1997