Using site semantics to analyze, visualize, and support navigation

Data Mining and Knowledge Discovery

Volumn 6, Issue 1, 2002, Pages 37-59

  Berendt, Bettina ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

Author keywords

Sequence mining; Statistical techniques; Visualization; Web usage mining

Indexed keywords

ONLINE SEARCHING; SEMANTICS; STATISTICAL METHODS; WEBSITES;

WEB USAGE MINING;

DATA MINING;

EID: 0141465120     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1013280719795     Document Type: Review

References (40)

1. Agrawal, R. and Srikant, R. 1995. Mining sequential patterns. In Proceedings of the International Conference on Data Engineering, Taipei, Taiwan, pp. 3-14.
2. Annacker, D., Spiekermann, S., and Strobel, M. 2001. Private consumer information: A new search cost dimension in online environments. In Proceedings of the 14th Bled Electronic Commerce Conference, Bled, Slovenia, June 25-26, 2001.
3. Baumgarten, M., Büchner, A.G., Anand, S.S., Mulvenna, M.D., and Hughes, J.G. 2000. User-driven navigation pattern discovery from Internet data. In Advances in Web Usage Analysis and User Profiling. Berlin: Springer, pp. 74-91.
4. Berendt, B. and Brenstein, E. 2001. Visualizing individual differences in web navigation: STRATDYN, a tool for analyzing navigation patterns. Behavior Research Methods, Instruments, & Computers, 33:243-257.
5. Berendt, B., Rauh, R., and Barkowsky, T. 1998. Spatial thinking with geographic maps: An empirical study. In Herausforderungen an die Wissensorganisation: Visualisierung, multimediale Dokumente, Internetstrukturen (Proceedings ISKO'97), H. Czap, H.-P. Ohly, and S. Pribbenow (Eds.). Würzburg, Germany: ERGON-Verlag, pp. 63-74.
6. Berendt, B. and Spiliopoulou, M. 2000. Analysis of navigation behaviour in web sites integrating multiple information systems. The VLDB Journal, 9:56-75.
7. Berry, M.J.A. and Linoff, G. 1997. Data Mining Techniques: For Marketing, Sales and Customer Support. New York: John Wiley & Sons, Inc.
8. Berthon, P., Pitt, L.F., and Watson, R.T. 1996. The world wide web as an advertising medium. Journal of Advertising Research, 36:43-54.
9. Borges, J. and Levene, M. 2000. Data mining of user navigation patterns. In Advances in Web Usage Analysis and User Profiling. Berlin: Springer, pp. 92-111.
10. Bortz, J. 1993. Statistik für Sozialwissenschaftler. 4th rev. ed.. Berlin: Springer.
11. Bresnahan, J.L. and Shapiro, M.M. 1966. A general equation and technique for the exact partitioning of chi-square contingency tables. Psychological Bulletin, 66:252-262.
12. Brin, S., Motwani, R., and Silverstein, C. Beyond market baskets: Generalizing association rules to correlations. In ACM SIGMOD International Conference on Management of Data, pp. 265-276.
13. Card, S.K., Mackinlay, J.D., and Shneiderman, B. 1999. Information visualization. In Readings in Information Visualization: Using Vision to Think, S.K. Card, J.D. Mackinlay, and B. Shneiderman (Eds.). San Francisco, CA: Morgan Kaufmann, pp. 1-34.
14. Chi, E.H., Pirolli, P., and Pitkow, J. 2000. The scent of a site: A system for analzing and predicting information scent, usage, and usability of a web site. In Proceedings of ACM CHI 2000 Conference on Human Factors in Computing Systems. Amsterdam: ACM Press, pp. 161-168.
15. Cooley, R. 2000. Web Usage Mining: Discovery and Application of Interesting Patterns from Web Data. University of Minnesota, Faculty of the Graduate School: Ph.D. dissertation. http://www.cs.umn.edu/research/websift/papers/rwc.thesis.ps
16. Cooley, R., Mobasher, B., and Srivastava, J. 1999. Data preparation for mining world wide web browsing patterns. Journal of Knowledge and Information Systems, 1:5-32.
17. Cooley, R., Tan, P.-N., and Srivastava, J. 2000. Discovery of interesting usage patterns from web data. In Advances in Web Usage Analysis and User Profiling. Berlin: Springer, pp. 163-182.
18. Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., and Slattery, S., 2000. Learning to construct knowledge bases from the world wide web. Artificial Intelligence, 118:69-113.
19. Cugini, J. and Scholtz, J. (1999). VISVIP: 3D Visualization of Paths through Web Sites. In Proceedings of the International Workshop on Web-Based Information Visualization (WebVis'99). Florence, Italy: IEEE Computer Society, pp. 259-263.
20. Fu, W.-T. 2001. ACT-PRO Action Protocol Analyzer: A tool for analyzing discrete action protocols. Behavior Research Methods, Instruments, & Computers, 33:149-158.
21. Green, M. 1998. Toward a Perceptual Science of Multidimensional Data Visualization: Bertin and Beyond. http://www.ergogero.com/dataviz/dviz0.html.
22. Han, J. and Kamber, M. 2001. Data Mining: Concepts and Techniques. San Francisco, LA: Morgan Kaufmann.
23. Jones, T. and Berger, C. 1995. Students' use of multimedia science instruction: Designing for the MTV generation? Journal of Educational Multimedia and Hyermedia, 4:305-320.
24. Mannila, H. and Toivonen, H. 1996. Discovering generalized episodes using minimal occurrences. In Proceedings of the 2nd International Conference KDD'96, pp. 146-151.
25. Menascé, D.A., Almeida, V., Fonseca, R., and Mendes, M.A. 1999. A methodology for workload characterization of E-commerce sites. In Proceedings of the ACM Conference on Electronic Commerce, Denver, CO, Nov. 1999.
26. Mobasher, B., Cooley, R., and Srivastava, J. (2000). Automatic personalization based on web usage mining. Communications of the ACM, 43:142-151.
27. Oberlander, J., Cox, R., Monaghan, P., Stenning, K., and Tobin, R. 1996. Individual differences in proof structures following multimodal logic teaching. In Proceedings COGSCI'96, pp. 201-206.
28. Olson, G.M., Herbsleb, I.D., and Rueter, H. 1994. Characterizing the sequential structure of interactive behaviors through statistical and grammatical techniques. Human-Computer Interaction, 9:427-472.
29. Page, L., Brin, S., Motwani, R., and Winograd, T. 1998. The PageRank Citation Ranking: Bringing Order to the Web. http://www-db.stanford.edu/̃backrub/pageranksub.ps.
30. Spiekermann, S., Grossklags, J., and Berendt, B. 2001. Stated privacy preferences versus actual behavior in EC environments: A reality check. In e-Finance. Innovative Problem1 ösungen für Informationssysteme in der Finanzwirtschaft. (Proceedings der 5. Internationalen Tagung Wirtschaftsinformatik 2001/3. Tagung Informatik in der Finanzwirtschaft), H.U. Buhl, N. Kreyer, and W. Steck (Eds.). Berlin: Springer, pp. 129-147.
31. Spiekermann, S. and Parachiv, C. 2000. Motivating human-agent interaction: Transferring insights from behavioral marketing to agent design. In Proceedings of the 3rd International Conference on Telecommunications and Electronic Commerce ICTEC3, pp. 387-402.
32. Spiliopoulou, M. 1999. The laborious way from data mining to web mining. International Journal of Computer Systems, Science, and Engineering, 14:113-126.
33. Spiliopoulou, M. and Berendt, B. 2001. Kontrolle der Präsentation und Vermarktung von Gütern im WWW anhand von Data-Mining-Techniken. In Handbuch Data Mining im Marketing, H. Hippner, U. Küsters, and M. Meyer (Eds.). Wiesbaden, Germany: Vieweg, pp. 855-873.
34. Spiliopoulou, M. and Faulstich, L.C. 1999. WUM: A tool for web utilization analysis. Extended version of Proceedings EDBT Workshop WebDB'98. Berlin: Springer, pp. 184-203.
35. Spiliopoulou, M. and Masand, B. (Eds.). Advances in Web Usage Analysis and User Profiling. Berlin: Springer, 2000.
36. Spiliopoulou, M. and Pohle, C. 2001. Data mining for measuring and improving the success of web sites. Data Mining and Knowledge Discovery, 5:85-14.
37. Spiliopoulou, M., Pohle, C., and Faulstich, L.C. 2000. Improving the effectiveness of a web site with web usage mining. In Advances in Web Usage Analysis and User Profiling. Berlin: Springer, pp. 142-162.
38. Srikant, R. and Agrawal, R. 1996. Mining sequential patterns: Generalizations and performance improvements. EDBT. Avignon, France, pp. 3-17.
39. Srivastava, J., Cooley, R., Deshpande, M., and Tan, P.-N. 2000. Web usage mining: Discovery and application of usage patterns from web data. SIGKDD Explorations, 1:12-23.
40. Wang, K. 1997. Discovering patterns from large and dynamic sequential data. Intelligent Information Systems, 9:8-33.