Weighted order-dependent clustering and visualization of web navigation patterns

Decision Support Systems

Volumn 43, Issue 4, 2007, Pages 1630-1645

  Kim, YongSeog ^a  

^a UTAH STATE UNIVERSITY   (United States)

Author keywords

Clustering; Markov state transition diagrams; Self organizing map; Visualization

Indexed keywords

CLUSTERING ALGORITHMS; DATA VISUALIZATION; DECISION MAKING; ELECTRONIC COMMERCE; MARKOV PROCESSES; SELF ORGANIZING MAPS;

CLUSTERING PROCESSES; MARKOV STATE-TRANSITION DIAGRAMS; WEB MARKETING;

PATTERN RECOGNITION;

EID: 34547805687     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2006.03.007     Document Type: Article

References (20)

1. Anandarajan M. Profiling Web usage in the workplace: a behavior-based artificial intelligence approach. Journal of Management Information Systems 19 1 (2002) 243-266
2. Azcarraga A., Hsieh M., and Setiono R. Visualizing globalization: a self-organizing map approach to customer profiling. Proc. of 24th Int'l Conf. on Information Systems (2003) 592-603
3. Bapna R., Goes P., Gupta A., and Jin Y. User heterogeneity and its impact on electronic auction market design: an empirical exploration. MIS Quarterly 28 1 (2004) 21-43
4. Cadez I., Heckerman D., Meek C., Smyth P., and White S. Model-based clustering and visualization of navigation patterns on a Web site. Data Mining and Knowledge Discovery 7 4 (2003) 399-424
5. Chaturvedi A., Carroll J.D., Green P.E., and Rotondo J.A. A feature-based approach to market segmentation via overlapping K-centroids clustering. Journal of Marketing Research 34 (1997) 370-377
6. Chen M.-S., Park J.S., and Yu P.S. Efficient data mining for traversal patterns. IEEE Transactions on Knowledge and Data Engineering 10 2 (1998) 209-221
7. Ester M., Kriegel H.-P., Sander J., Wimmer M., and Xu X. Incremental clustering for mining in a data warehousing environment. Proc. of 24th Int'l Conf. on Very Large Data Bases (VLDB'98), New York City, NY (1998) 323-333
8. Fu Y., Sandhu K., and Shih M.-Y. Clustering of Web users based on access patterns. Lecture Notes in Artificial Intelligence 1836 (2000) 21-38
9. Hartigan J.A. Clustering Algorithms (1975), John Wiley and Sons, New York
10. Hay B., Wets G., and Vanhoof K. Segmentation of visiting patterns on web sites using a sequence alignment method. Journal of Retailing and Consumer Services 10 (2003) 145-153
11. Jain A., Murty M., and Flynn P. Data clustering: A review. ACM Computing Surveys 31 3 (1999) 264-323
12. S. Kaski, Data exploration using self-organizing maps, PhD thesis, Helsinki University of Technology, Finland, Acta Polytechnica Scandinavica Mathematics, Computing and Management in Engineering No. 82 (1997).
13. Kohonen T. Self-Organizing Maps (1997), Springer-Verlag, Berlin
14. Mobasher B., Cooley R., and Srivastava J. Automatic personalization based on Web usage mining. Communications of the ACM 43 8 (2000) 84-90
15. Perkowitz M., and Etzioni O. Toward adaptive Web sites: Conceptual framework and case study. Artificial Intelligence 118 1-2 (2000) 245-275
16. Shahabi C., Zarkesh A.M., Adibi J., and Shah V. Knowledge discovery from users web-page navigation. Proc. of Workshop on Research Issues in Data Engineering (RIDE97) (1997) 20-29
17. Smith K.A., and Ng A. Web page clustering using a self-organizing map of user navigation patterns. Decision Support Systems 35 (2003) 245-256
18. Smyth P. Clustering sequences with hidden Markov models. In: Mozer M.C., Jordan M.I., and Petsche T. (Eds). Advances in Neural Information Processing Systems vol. 9 (1997), MIT Press 648-654
19. Spiliopoulou M., Pohle C., and Faulstich L. Improving the effectiveness of a Web site with Web usage mining. In: Masand B., and Spiliopoulou M. (Eds). Advances in Web Usage Analysis and User Profiling (2000), Springer-Verlag, Berlin 142-162
20. Yan T.W., Jacobsen M., Garcia-Molina H., and Dayal U. From user access patterns to dynamic hypertext linking. Computer Networks 28 7-11 (1996) 1007-1014