Name
Abstract | ||
|---|---|---|
Knowledge Discovery from the secondary data generated by the user interactions with the web has become very critical for effective and efficient managing of the activities related to e-business, e-services-education, personalization, web site management and so on. Mining Web access logs that contain substantial data about user access patterns on one or more web localities is an emerging research area. One of the important phases in mining user access patterns is the clustering of web users. In this paper, we present an approach to dynamically group web users based on their web access patterns using Adaptive Resonance Theory Neural Network. Knowledge extracted from web user clusters has been used for prefetching of pages between web clients and proxies. Experiments have been conducted and the results have shown that our ART based clustering approach performed better in terms of intra-cluster distances. |
Year | Venue | Keywords |
|---|---|---|
2005 | Proceedings of the IASTED International Conference on Computational Intelligence | knowledge discovery, ART neural networks, clustering, web usage mining |
Field | DocType | Citations |
Nervous system network models,Computer science,Time delay neural network,Artificial intelligence,Artificial neural network,Cluster analysis,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. T. Raju | 1 | 0 | 2.37 |
| Suresh Khandige | 2 | 0 | 0.34 |