Decision trees for web log mining

Intelligent Data Analysis

Volumn 7, Issue 2, 2003, Pages 141-154

  Pabarskaite, Zidrina ^a  

^a VILNIUS UNIVERSITY   (Lithuania)

Author keywords

association rules; data pre processing; decision trees; web log data; web usage mining

Indexed keywords

ASSOCIATION RULES; BEHAVIORAL RESEARCH; BLOGS; DATA HANDLING; DATA MINING; DECISION TREES; WEBSITES;

COMPUTATIONAL PERFORMANCE; DATA ANALYSTS; DATA PREPROCESSING; LARGE AMOUNTS OF DATA; MISCLASSIFICATION ERROR; WEB LOG DATA; WEB LOG MINING; WEB USAGE MINING;

TREES (MATHEMATICS);

EID: 33646341253     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2003-7205     Document Type: Article

References (38)

1. R. Agrawal et al., Mining association rules between sets in large databases, in Conference on Managment of Data (ACM SIGMOD), Washington, DC, 1993.
2. R. Agrawal et al., Mining Sequential Patterns, in Data engineering, Ieee, Taipei, 1995.
3. Analog, http://www.analog.cx/.
4. S. Ansari et al., Integrating E-Commerce and Data Mining: Architecture and Challenges, in Data mining, IEEE Computer Society, San Jose, CA, 2001.
5. M. Balabanovic et al., An Adaptive Agent for Automated Web Browsing, Journal of Visual Communication and Image Representation 6(4) (1995).
6. B. Berendt et al., Analysis of navigation behaviour in web sites integrating multiple information systems, VLDB 9 (2000), 56-75.
7. D. Boley et al., Web Page Categorization and Feature Selection Using Association Rule and Principal Component Clustering, in 7th Workshop on Information Technologies and System (WITS'97), 1997.
8. J. Borges et al., Data Mining of User Navigation Patterns, in WEBKDD '99; Web usage analysis and user profiling, Berlin, San Diego, CA, 1999.
9. L.D. Catledge et al., Characterizing browsing strategies in the World-Wide Web, Computer Networks and Isdn Systems 27(6) (1995), 1065.
10. M.S. Chen et al., Efficient Data Mining for Path Traversal Patterns, Ieee Transactions on Knowledge and Data Engineering 10(2) (1998), 209-221.
11. E.H. Chi, Improving Web Usability Through Visualization, IEEE Internet Computing (2002), 64-71.
12. R. Cooley et al., Data Preparation for Mining World Wide Web Browsing Patterns, Knowledge and Information Systems 1(1) (1999).
13. DataMiningSuite, http://www.datamining.com/dmsuite.htm.
14. R.O. Duda et al., Pattern Classification, 2001.
15. L. Faulstich et al., A Data Mining Analyzing the Navigational Behaviour of Web Users, in Workshop on Machine Learning User Modelling of the ACAI'99 International Conf., Creta, Greece, 1999.
16. L.C. Faulstich et al., WUM: A Tool for Web Utilization Analysis, in EDBT Workshop WebDB'98, Springer Verlag, Valencia, Spain, 1998.
17. S.H. Ha et al., C4.7 Web Mining for Distance Education, in International conference on management of innovation and technology; Management in the 21st century, Ieee, Singapore, 2000.
18. J. Han et al., Mining Access Pattern efficiently from Web logs, in Pasific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD'00), Kyoto, Japan, 2000.
19. J. Han et al., Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs, in Conf. on Advances in Digital Libraries, Santa Barbara, CA, 1998.
20. D. Hand et al., Principles of data mining, 2001.
21. N. Jain et al., Web Mining: Pattern Discovery from World Wide Web Transactions, University of Minesota, Minneapolis, 1997.
22. K.V. Kanth Ravi et al., Personalization and Location-based Technologies for E-commerce Applications.
23. R. Kosala et al., Web Mining Research: A Survey, SIGKDD Explorations 2(1) (2000), 1-15.
24. S.K. Madria et al., Research Issues in Web Data Mining, in Data warehousing and knowledge discovery, Florence, Italy, Berlin, 1999.
25. A.L. Montgomery et al., Identifying Web Browsing Trends and Patterns, Computer 34(7) (2001), 94-95.
26. net.Analysis, http://www.netgen.com/.
27. Z. Pabarskaite, Implementing Advanced Cleaning and End-User Interpretability Technologies in Web Log Mining, in Information Technology Interfaces (ITI2002), Cavtat, Croatia, 2002.
28. Z. Pabarskaite et al., Advances in Web Usage Mining, in The 6th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2002), Florida, USA, 2002.
29. M. Perkowitz et al., Towards Adaptive Web Sites: Conceptual Framework and Case Study, in Eighth International Worl Wide Web Conference, Toronto, Ontario, 1999.
30. J. Pitkow, In Search of Reliable Usage Data on theWWW, in The Sixth International World Wide Web Conference, Santa Clara, CA, 1997.
31. J. Pitkow et al., A Simple Yet Robust Caching Algorithm Based on Dynamic Access Patterns, in First International Worl Wide Web Conference, CERN, Geneva, Switzerland, 1994.
32. J.R. Quinlan, C4.5: programs for machine learning, M. Kaufmann, ed., San Mateo, CA, 1993.
33. T. Savola et al., Using HTML, 1996.
34. E. Schmitt et al., Measuring Web Success, Forrester Research, 1999.
35. R. Srikant et al., Mining Sequential Patterns: Generalizations and Performance Improvements, in Extending database technology, Avignon, France: Springer, 1996.
36. S. Webhound, http://www.sas.com/products/webhound/index.html.
37. Webtrends, http://www.webtrends.com/.
38. O.R. Zaiane, Resource and Knowledge Discovery From The Internet And Multimedia Repositories, in School of Computing Science, Simon Fraser University: Quebec, 1999, pp. 268.