The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.05 - May (2008 vol.20)
pp: 653-667
ABSTRACT
Web users clustering is a crucial task for mining information related to users needs and preferences. Up to now, popular clustering approaches build clusters based on usage patterns derived from users' page preferences. This paper emphasizes the need to discover similarities in users' accessing behavior with respect to the time locality of their navigational acts. In this context, we present two time aware clustering approaches for tuning and binding the page and time visiting criteria. The two tracks of the proposed algorithms define clusters with users that show similar visiting behavior at the same time period, by varying the priority given to page or time visiting. The proposed algorithms are evaluated using both synthetic and real datasets and the experimentation has shown that the new clustering schemes result in enriched clusters compared to those created by the conventional non-time aware users clustering approaches. These clusters contain users exhibiting similar access behavior not only in terms of their page preferences but also of their access time.
INDEX TERMS
Web mining, Web users clustering, Navigation, access time
CITATION
Sophia G. Petridou, Vassiliki A. Koutsonikola, Athena I. Vakali, Georgios I. Papadimitriou, "Time-Aware Web Users' Clustering", IEEE Transactions on Knowledge & Data Engineering, vol.20, no. 5, pp. 653-667, May 2008, doi:10.1109/TKDE.2007.190741
REFERENCES
[1] I. Cadez, D. Heckerman, C. Meek, P. Smyth, and S. White, “Model-Based Clustering and Visualization of Navigation Patterns on a Web Site,” J. Data Mining and Knowledge Discovery, vol. 7, no. 4, pp. 399-424, 2003.
[2] G. Pallis, L. Angelis, and A. Vakali, “Model-Based Cluster Analysis for Web Users Sessions,” Proc. 15th Int'l Symp. Methodologies for Intelligent Systems (ISMIS '05), pp. 219-227, May 2005.
[3] G. Xu, Y. Zhang, J. Ma, and X. Zhou, “Discovering User Access Pattern Based on Probabilistic Latent Factor Model,” Proc. 16th Australasian Database Conf. (ADC '05), pp. 27-35, Jan. 2005.
[4] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2001.
[5] Y. Zhao and G. Karypis, “Criterion Functions for Document Clustering: Experiments and Analysis,” technical report, Dept. Computer Science, Univ. of Minnesota, 2001.
[6] A. Bianco, G. Mardente, M. Mellia, M. Munafo, and L. Muscariello, “Web User Session Characterization via Clustering Techniques,” Proc. IEEE GLOBECOM '05, p. 6, Dec. 2005.
[7] S. Petridou, V. Koutsonikola, A. Vakali, and G. Papadimitriou, “A Divergence-Oriented Approach for Web Users Clustering,” Proc. Int'l Conf. Computational Science and Its Applications (ICCSA '06), pp. 1229-1238, May 2006.
[8] D. Hand, H. Mannila, and P. Smyth, Principles of Data Mining. MIT Press, 2001.
[9] M. Albanese, A. Picariello, C. Sansone, and L. Sansone, “Web Personalization Based on Static Information and Dynamic User Behavior,” Proc. Sixth ACM Int'l Workshop Web Information and Data Management (WIDM '04), pp. 80-87, Nov. 2004.
[10] J. Xiao and Y. Zhang, “Clustering of Web Users Using Session-Based Similarity Measures,” Proc. Int'l Conf. Computer Networks and Mobile Computing (ICCNMC '01), p. 223, Oct. 2001.
[11] A. Banerjee and J. Ghosh, “Clickstream Clustering Using Weighted Longest Common Subsequences,” Proc. Workshop Web Mining, SIAM Conf. Data Mining, pp. 33-40, 2001.
[12] P. Lingras and C. West, “Interval Set Clustering of Web Users with Rough-Means,” J. Intelligent Information Systems, vol. 23, no. 1, pp.5-16, 2004.
[13] A.K. Jain, M.N. Murty, and P.J. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999.
[14] D. Knuth, The Art of Computer Programming. Addison-Wesley, 1973.
[15] R. Duda and P. Hart, Pattern Classification and Scene Analysis. John Wiley & Sons, 1973.
[16] K. Gowda and E. Diday, “Symbolic Clustering Using a New Dissimilarity Measure,” IEEE Trans. Systems Man and Cybernetics, vol. 22, pp. 368-378, 1992.
[17] R. Keeney and H. Raiffa, Decisions with Multiple Objectives: Preferences and Value Tradeoffs. John Wiley & Sons, 1976.
[18] K. Schloegel, G. Karypis, and V. Kumar, “A New Algorithm for Multi-Objective Graph Partitioning,” Proc. Fifth Int'l Euro-Par Conf., pp. 322-331, Aug./Sept. 1999.
[19] Y. Zhao and G. Karypis, “Topic-Driven Clustering for Document Datasets,” Proc. SIAM Int'l Conf. Data Mining, pp. 358-369, Apr. 2005.
[20] M. Garey and D. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, 1979.
[21] J. Srivastava, R. Cooley, M. Deshpande, and P. Tan, “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data,” SIGKDD Explorations, vol. 1, no. 2, pp. 12-23, 2000.
[22] D. Andrews, “Plots of High-Dimensional Data,” Biometrics, vol. 28, pp. 125-136, 1972.
[23] T. Theodosiou, L. Angelis, A. Vakali, and G.N. Thomopoulos, “Gene Functional Annotation by Statistical Analysis of Biomedical Articles,” Int'l J. Medical Informatics, submitted for publication.
[24] S. Boll, “Modular Content Personalization Service Architecture for E-commerce Applications,” Proc. Fourth IEEE Int'l Workshop Advanced Issues of E-commerce and Web-Based Information Systems (WECWIS '02), pp. 213-220, June 2002.
[25] W. Yang, Z. Wang, and M. You, “An Improved Collaborative Filtering Method for Recommendations' Generation,” Proc. Int'l Conf. Systems, Man and Cybernetics (SMC '04), pp. 4135-4139, Oct. 2004.
[26] K.-L. Wu, C. Aggarwal, and P. Yu, “Personalization with Dynamic Profiler,” Proc. Third Int'l Workshop Advanced Issues of E-commerce and Web-Based Information Systems (WECWIS '01), pp. 12-20, June 2001.
[27] J. Xu, J. Liu, B. Li, and X. Jia, “Caching and Prefetching for Web Content Distribution,” Computing in Science and Eng., vol. 6, no. 4, pp. 54-59, 2004.
6 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool