Mining fuzzy association rules for web access case adaptation

Web access path prediction using knowledge discovered from web logs has become an active research area. Web logs provide updated information about the user’s access record to a web site, which contains useful patterns waiting to be discovered and used for improving the web site. In this study, a new approach to web access pattern prediction is proposed. The methodology is based on the case-based reasoning approach, and the main idea is to discover user access patterns by mining the fuzzy association rules from the historical web log data. In our approach, the time duration of each user session is also considered as one of the attributes of a web access case. A fuzzy index tree is used for fast matching of rules. Furthermore, the system's performance is enhanced using the information contained in the user profile through an adaptation process.

[1]  Jaideep Srivastava,et al.  Discovery of Interesting Usage Patterns from Web Data , 1999, WEBKDD.

[2]  Qiang Yang,et al.  Taylor series prediction: a cache replacement policy based on second-order trend analysis , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[3]  Qiang Yang,et al.  WhatNext: a prediction system for Web requests using n-gram sequence models , 2000, Proceedings of the First International Conference on Web Information Systems Engineering.

[4]  Jian Pei,et al.  Mining Access Patterns Efficiently from Web Logs , 2000, PAKDD.

[5]  Attila Gyenesei,et al.  A Fuzzy Approach for Mining Quantitative Association Rules , 2000, Acta Cybern..

[6]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[7]  Mark Levene,et al.  Data Mining of User Navigation Patterns , 1999, WEBKDD.

[8]  Anupam Joshi,et al.  Robust Fuzzy Clustering Methods to Support Web Mining , 1998 .

[9]  A Tour Guide for the World Wide Web , 1997 .

[10]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[11]  Alex G. Büchner.  Discovering Internet Marketing Intelligence through Web Log Mining , 2003 .

[12]  Anupam,et al.  Mining Web Access Logs Using Relational Competitive Fuzzy Clustering , 1999 .

[13]  WebUsersMyra Spiliopoulou,et al.  A Data Miner analyzing the Navigational Behaviour of , 1999 .

[14]  Elias N. Houstis,et al.  On disconnected browsing of distributed information , 1997, Proceedings Seventh International Workshop on Research Issues in Data Engineering. High Performance Database Management for Large-Scale Applications.

[15]  Qiang Yang,et al.  A prediction system for multimedia pre-fetching in Internet , 2000, ACM Multimedia.