Dynamic Grouping of Web Users Based on Their Web Access Patterns using ART1 Neural Network Clustering Algorithm

In this paper, we propose ART1 neural network clustering algorithm to group users according to their Web access patterns. We compare the quality of clustering of our ART1 based clustering technique with that of the K-Means and SOM clustering algorithms in terms of inter-cluster and intra-cluster distances. The results show the average inter-cluster distance of ART1 is high compared to K-Means and SOM when there are fewer clusters. As the number of clusters increases, average inter-cluster distance of ART1 is low compared to K-Means and SOM which indicates the high quality of clusters formed by our approach.

[1]  Duncan Dubugras Alcoba Ruiz,et al.  A pre-processing tool for Web usage mining in the distance education domain , 2004, Proceedings. International Database Engineering and Applications Symposium, 2004. IDEAS '04..

[2]  Jaideep Srivastava,et al.  Creating adaptive Web sites through usage-based clustering of URLs , 1999, Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453).

[3]  Ron Kohavi,et al.  Ten Supplementary Analyses to Improve E-commerce Web Sites , 2003 .

[4]  PatternsYongjian,et al.  Clustering of Web Users Based on Access , 1999 .

[5]  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.

[6]  S. Sitharama Iyengar,et al.  Faster Web Page Allocation with Neural Networks , 2002, IEEE Internet Comput..

[7]  Georgios Paliouras,et al.  Clustering the Users of Large Web Sites into Communities , 2000, ICML.

[8]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[9]  Vir V. Phoha,et al.  Web user clustering from access log using belief function , 2001, K-CAP '01.

[10]  Myra Spiliopoulou,et al.  The Impact of Site Structure and User Environment on Session Reconstruction in Web Usage Analysis , 2002, WEBKDD.

[11]  Kyuseok Shim,et al.  Data mining and the Web: past, present and future , 1999, WIDM '99.

[12]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[13]  Hendrik Blockeel,et al.  Web mining research: a survey , 2000, SKDD.

[14]  Padhraic Smyth,et al.  Visualization of navigation patterns on a Web site using model-based clustering , 2000, KDD '00.

[15]  Jiawei Han,et al.  Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs , 1998, Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries -ADL'98-.