Categorizing visitors dynamically by fast and robust clustering of access logs

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

Volumn 2198, Issue , 2001, Pages 498-507

  Estivill Castro, Vladimir ^a   Yang, Jianhua ^a  

^a UNIVERSITY OF NEWCASTLE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; EXHIBITIONS; IMAGE SEGMENTATION; ITERATIVE METHODS; MAXIMUM PRINCIPLE; WORLD WIDE WEB;

ATTRIBUTE VECTORS; EXPECTATION - MAXIMIZATIONS; ITERATIVE ALGORITHM; LARGE DIMENSIONS; LARGE VOLUMES; ROBUST CLUSTERING; SUBQUADRATIC COMPLEXITY; WEB APPLICATION;

CLUSTERING ALGORITHMS;

EID: 26344435844     PISSN: 03029743     EISSN: 16113349     Source Type: Journal    
DOI: 10.1007/3-540-45490-x_64     Document Type: Article

References (30)

1. C. Bajaj. Proving geometric algorithm non-solvability: An application of factoring polynomials. Journal of Symbolic Computation, 2:99-102, 1986.
2. J. Borges and M. Levene. Mining assocaition rules in hypertext databases. R. Agrawal, ed., 4th Int. Conf. on KDD, 149-153, NY, August 27-31 1998.
3. C. R. Cunha and C. F. B. Jaccound. Determining WWW user’s next access and its application to prefetching. Int. Symp. on Computers and Communication’97, Alexandria, Egypt, July, 1997.
4. A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likehood from incomplete data via the EM algorithm. J. Royal Statistical Society B, 39:1-38, 1977.
5. M. Ester, H.P. Kriegel, S. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. E. Simoudis, et al eds., 2nd Int. Conf. KDD, 226-231, Menlo Park, CA, 1996. AAAI Press.
6. V. Estivill-Castro and M.E. Houle. Roboust clustering of large geo-referenced data sets. N. Zhong & L. Zhow, eds., 3rd PAKDD-99, 327-337. LNAI 1574, 1999.
7. V. Estivill-Castro and M.E. Houle. Fast randomized algorithms for robust estimation of location. TSDM2000, 74-85, Lyon, 2000. LNAI 2007.
8. V. Estivill-Castro and A.T. Murray. Discovering associations in spatial data - an efficient medoid based approach. X. Wu, et al, eds., 2nd PAKDD-98, 110-121, Melbourne, Australia, 1998. LNAI 1394.
9. I.B. Hall, L.O. ¨ Ozyurt and J.C. Bezdek. Clustering with a genetically optimized approach. IEEE T. on Evolutionary Computation, 3(2):103-112, 1999.
10. T. Kato, H. Nakyama and Y. Yamane. Navigation analysis tool based on the correlation between contents and access patterns. Manuscript. http://citeseer.nj.nec.com/354234.html.
11. J. MacQueen. Some methods for classification and analysis of multivariate observations. L. Le Cam and J. Neyman eds., 5th Berkley Symp. on Mathematical Statistics and Probability, 281-297, 1967. Volume 1.
12. B. Mobasher, H. Dai, T. Luo, M. Nakagawa, Y. Sun, and J. Wilshire. Discovery of aggregate usage profiles for web personalization. WEB Mining for E-Commerce Workshop Web KDD-2000, Boston, August 2000.
13. T. Morzy, M. Wojciechowski, and Zakrzewicz. Scalabale hierarchical clustering methods for sequences of categorical values. D. Cheung, et al eds., 5th PAKDD, 282-293, Hong Kong, 2001. LNAI 2035.
14. F. Murtagh. Comments of “Parallel algorithms for hierarchical clustering and cluster validity”. IEEE T. on Pattern Analysis and Machine Intelligence, 14(10):1056-1057, 1992.
15. R.T. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. 20th VLDB, 144-155, 1994. Santiago, Chile, Morgan Kaufmann.
16. G. Paliouras, C. Papatheodorou, V. Karkaletsis, and C. Spyropoulos. Clustering the users of large web sites into communities. P. Langley, ed., 17th Int. Conf. on Machine Learning, 719-726, 2000. Morgan Kaufmann.
17. J. Pei, J. Han, B. Mortazavi-asl, and H. Zhu. Mining access patterns efficiently from web logas. T. Terano, et al, eds., 4th PAKDD, 396-407, Kyoto, 2000. LNCS 1805.
18. D.M. Pennock, E. Horvitz, S. Lawrence, and C.L. Giles. Collaborative filtering by personality diagnosis: A hybrid memory- and model-based approach. 16th Conf. on Uncertanity in Artificial Intelligence, 473-840, 2000. Morgan Kaufmann.
19. M Perkowitz and O. Etzioni. Adaptive web sites: an AI challenge. IJCAI, 16-23, Nagoya, Japan, 1998.
20. M Perkowitz and O. Etzioni. Adaptive web sites: Automatically synthesizing web pages. 15th National Conf. on Artificial Intelligence, 727-732, 1998. AAAI Press.
21. P.J. Rousseeuw and A.M. Leroy. Robust regression and outlier detection. Wiley, NY, 1987.
22. C. Shahabi, A. M. Zarkesh, J. Adibi, and V. Shah. Knowledge discovery from users web page navigation. P. Schevemann, ed., Int. Workshop on Research Issues in Data Engineering IEEE RIDE’97, 20-31, 1997.
23. M. Spiliopoulou. Web usage mining for web site evaluation. Communication of the ACM, 43(8):127-134, 2000.
24. J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Esplorations, 1(2):12-23, January 2000.
25. M.B. Teitz and P. Bart. Heuristic methods for estimating the generalized vertex median of a weighted graph. Operations Research, 16:955-961, 1968.
26. W. Wang, J. Yang, and R. Muntz. STING: A statistical information grid approach to spatial data mining. 23rd VLDB, 186-195, Athens, 1997. Morgan Kaufmann.
27. J. Xiao, Y. Zhang, X. Jia, and T. Li. Measuring similarity of interests for clustering web-users. In M.E. Orlowsak & J.F. Roddick, eds., 12th Australian Database Conf. ADC 2001, 107-114, Gold Coast, 2001. IEEE Computer Society.
28. A.M. Zarkesh, J. Adibi, C. Shahabi, R. Sadri, and V. Shah. Analysis and design of server informative WWW-sites. In K. Golshani, F.; Makki, editor, 6th ACM CIKM, 254-261, Las Vegas, 1997.
29. B. Zhang, M. Hsu, and U. Dayal. K-harmonic means — a spatial clustering algorithm with boosting. TSDM2000, 31-42, Lyon, 2000. LNAI 2007.
30. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient data clustering method for very large databases. SIGMOD Record, 25(2):103-114, June 1996.