A new approach to web users clustering and validation: A divergence-based scheme

International Journal of Web Information Systems

Volumn 5, Issue 3, 2009, Pages 348-371

  Koutsonikola, Vassiliki A ^a   Petridou, Sophia G ^a   Vakali, Athena I ^a   Papadimitriou, Georgios I ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Cluster analysis; Data mining; Internet; User studies

Indexed keywords

CLUSTERING PROBLEMS; DESIGN/METHODOLOGY/APPROACH; GRAPHICAL REPRESENTATIONS; INFORMATION-THEORETIC DISTANCE; KULLBACK-LEIBLER DIVERGENCE; PROBABILISTIC MEASURES; SYNTHETIC AND REAL DATA; USER STUDY;

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; DATA MINING; INTERNET;

WEBSITES;

EID: 84872961177     PISSN: 17440084     EISSN: None     Source Type: Journal    
DOI: 10.1108/17440080910983583     Document Type: Article

References (42)

1. Baeza-Yates, R. and Frakes, W. (1992), Information Retrieval: Data Structures and Algorithms, Prentice-Hall, Upper Saddle River, NJ.
2. Boutin, F. and Hascoer, M. (2004), "Cluster validity indices for graph partitioning,", Proceedings of the 8th IEEE International Conference on Information Visualisation, London, pp. 376-81.
3. Cadez, I., Heckerman, D., Meek, C., Smyth, P. and White, S. (2002), "Visualization of navigation patterns on a website using model-based clustering,", Technical Report MSR-TR-00-18, Microsoft Research.
4. Castellano, G., Fanelli, A.M., Mencar, C. and Torsello, M.A. (2007), "Similarity-based fuzzy clustering for user profiling,", IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, pp. 75-8.
5. Charikar, M., Guha, S., Tardos, E. and Shmoys, D. (1999), "A constant-factor approximation algorithm for the k-median problem,", Proceedings of the 31st Annual ACM Symposium on Theory of Computing, (STOC), ACM, Atlanta, GA, May 1-4, pp. 1-10.
6. Cover, T. and Thomas, J. (1991), Elements of Information Theory, Wiley, New York, NY.
7. Davies, D. and Bouldin, D. (1979), "A cluster separation measure,", IEEE Transactions on Pattern Analysis and Machine Learning, Vol. 1 No. 4, pp. 224-7.
8. Dhillon, I., Mallela, S. and Kumar, R. (2002), "Enhanced word clustering for hierarchical text classification,", Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, Edmonton, Canada, July 23-26, pp. 191-200.
9. Dunn, J.C. (1974), "Well separated clusters and optimal fuzzy partitions,", Journal Cybern, Vol. 4 No. 3, pp. 95-104.
10. Fung, G. (2001), "A comprehensive overview of basic clustering algorithms,", Technical Report, University of Winsconsin, Madison, WI.
11. Garey, M. and Johnson, D. (1979), Computers and Intractability: A Guide to the Theory of NP-Completeness, W.H. Freeman, New York, NY.
12. Gunter, S. and Bunke, H. (2003), "Validation indices for graph clustering,", Pattern Recognition Letters, Vol. 24 No. 8, pp. 1107-13. (Pubitemid 36189564)
13. Halkidi, M., Batistakis, Y. and Vazirgiannis, M. (2002a), "Cluster validity methods: part I,", SIGMOD Record, Vol. 31 No. 12, pp. 40-5.
14. Halkidi, M., Batistakis, Y. and Vazirgiannis, M. (2002b), "Clustering validity checking methods: part II,", SIGMOD Record, Vol. 31 No. 3, pp. 19-27.
15. Jain, A., Murty, M. and Flynn, P. (1999), "Data clustering: a review,", ACM Computing Surveys, Vol. 31 No. 3, pp. 264-323.
16. Kasturi, J., Acharya, R. and Ramanathan, M. (2003), "An information theoretic approach for analyzing temporal patterns of gene expression,", Bioinformatics, Vol. 19 No. 4, pp. 449-58. (Pubitemid 36336510)
17. Kerr, M. and Churchill, A. (2001), "Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments,", Proceedings of National Academy of Sciences of the United States of America 98, pp. 8961-5. (Pubitemid 32743852)
18. Lazzerini, B., Marcelloni, F. and Cococcioni, M. (2003), "A system based on hierarchical fuzzy clustering for web users profiling,", IEEE International Conference on Systems, Man and Cybernetics, pp. 1995-2000.
19. Liu, X., He, P. and Yang, Q. (2005), "Mining user access patterns based on web logs,", Canadian Conference on Electrical and Computer Engineering, pp. 2280-3. (Pubitemid 44802083)
20. McQueen, J. (1967), "Some methods for classification and analysis of multivariate observations,", Proceedings of the 5th Berkely Symposium on Mathematical Statistics and Probability, Berkeley, CA, June-July, Vol. 1, pp. 281-97.
21. Mojica, J.A., Rojas, D.A., Gomez, J. and Gonzalez, F. (2005), "Page clustering using a distance based algorithm,", paper presented at the 3rd Latin American Web Congress, October 31-November 2, p. 7.
22. Petridou, S., Koutsonikola, V., Vakali, A. and Papadimitriou, G. (2006), "A divergence-oriented approach for web users clustering,", Proceedings of International Conference on Computational Science and its Applications (ICCSA 2006), Glasgow, pp. 1229-38. (Pubitemid 44045429)
23. Petridou, S., Koutsonikola, V., Vakali, A. and Papadimitriou, G. (2008), "Time aware web users clustering,", IEEE Transactions on Knowledge and Data Engineering, Vol. 20 No. 5, pp. 653-67.
24. Shokry, R.A., Saad, A.A., El-Makkey, N.M. and Lsmail, M.A. (2006), "Using new soft clustering technique in adaptive web site,", IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 281-6.
25. Srivastava, J., Cooley, R., Deshpande, M. and Tan, P.-N. (2000), "Web usage mining: discovery and applications of usage patterns from web data,", SIGKDD Explorations, Vol. 1 No. 2, pp. 12-23.
26. Stein, B., Meyer Zu Eissen, S. and Wibrock, F. (2003), "On cluster validity and the information need of users,", 3rd IASTED International Conference on Artificial Intelligence and Applications (AIA 03), ACTA Press, Benalḿdena, Spain, September 8-10, pp. 216-21.
27. Sturn, A. (2001), "Cluster analysis for large scale gene expression studies,", Master's thesis, Graz University of Technology, Graz.
28. Vakali, A., Pokorny, J. and Dalamagas, T. (2004), "An overview of web data clustering practices,", Proceedings of the EDBT 2004 Workshop, Lecture Notes in Computer Science (LNCS) Series, Springer Verlag, Heraklion, pp. 597-606. (Pubitemid 39750591)
29. Vakali, A., Pallis, G. and Angelis, L. (2006), "Clustering web information sources, published in the book,", in Vakali, A. and Pallis, G. (Eds), Web Data Management Practices: Emerging Techniques and Technologies, Idea Group Publishing, Hershey, PA, pp. 34-55.
30. Wijaya, D.T. and Bressan, S. (2006), "Clustering web documents using co-citation, coupling, incoming, and outgoing hyperlinks: a comparative performance analysis of algorithms,", International Journal of Web Information Systems, Vol. 2 No. 2, pp. 69-76.
31. Xu, R. and Wunsch, D.I. (2005), "Survey of clustering algorithms,", IEEE Transactions on Neural Networks, Vol. 16 No. 3, pp. 645-78. (Pubitemid 40718010)
32. Yang, Y. and Padmanabhan, B. (2005), "GHIC: a hierarchical pattern-based clustering algorithm for grouping web transactions,", IEEE Transactions on Knowledge and Data Engineering, Vol. 17 No. 9, pp. 1300-4. (Pubitemid 46488946)
33. Zeng, H.-J., Chen, Z. and Ma, Y.-M. (2002), "A unified framework for clustering heterogeneous web objects,", Proceedings of the 3rd International Conference on Web Information Systems Engineering, IEEE Press, Grand. Hyatt, Singapore, pp. 161-72.
34. Zhang, K. (1995), "Algorithms for the constrained editing distance between ordered labeled trees and related problems,", Pattern Recognition, Vol. 28 No. 3, pp. 463-74.
35. Zhao, Y. and Karypis, G. (2005), "Topic-driven clustering for document datasets,", Proceedings of the SIAM International Conference on Data Mining, Newport Beach, CA, pp. 358-69.
36. Agrawal, R. and Srikant, R. (1994), "Fast algorithms for mining association rules,", Proceedings of the 20th International Conference on Very Large Databases, Santiago, pp. 487-99.
37. Eirinaki, M. and Vazirgiannis, M. (2003), "Web mining for web personalization,", ACM Transactions on Internet Technology, Vol. 3 No. 1, pp. 1-27.
38. Mannila, H., Toivonen, H. and Verkamo, A. (1995), "Discovering frequent episodes in sequences,", Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining, (KDD-95), American Association for Artificial Intelligence, Menlo Park, CA, pp. 210-5.
39. Schockaert, S., De Cock, M., Cornelis, C. and Kerre, E.E. (2007), "Clustering web search results using fuzzy ants,", International Journal of Intelligent Systems, Vol. 22 No. 5, pp. 455-74. (Pubitemid 46652944)
40. Shen, D., Chen, Z., Yang, Q., Zeng, H.-Z., Zhang, B., Lu, Y. and Ma, W.-Y. (2004), "Text classification: web-page classification through summarization,", Proceedings of the 27th ACM International Conference on Research and Development in Information Retrieval, SIGIR'04, Sheffield, July 25-29, pp. 242-9.
41. Vakali, A. and Papadimitriou, G. (2004), "Web engineering: the evolution of new technologies,", Guest Editorial in IEEE Computing in Science and Engineering, Vol. 6 No. 4, pp. 10-11.
42. Zhang, Y.J. and Liu, Z.Q. (2004), "Refining web search engine results using incremental clustering,", International Journal of Intelligent Systems, Vol. 19 Nos 1/2, pp. 191-9.