A novel approach for extraction of cluster patterns from Web Usage Data and its performance analysis

Majority of the techniques that have been used for pattern discovery from Web Usage Data (WUD) are clustering methods. In e-commerce applications, clustering methods can be used for the purpose of generating marketing strategies, product offerings, personalization and Web site adaptation. A novel Partitional based approach for dynamically grouping Web users based on their Web access patterns using Adaptive Resonance Theory1 Neural Network(ART1 NN) clustering algorithm is presented in this paper. The problem formulation and the proposed ART1 NN clustering methodology have been discussed. Experimental results shows that our ART1 NN clustering approach performs better in terms of intra-cluster and inter-cluster distances compared to K-Means and SOM clustering algorithms.