Integration of ART2 neural network and genetic K-means algorithm for analyzing Web browsing paths in electronic commerce

In order to verify the proposed method, data from a Monte Carlo Simulation are used. The simulation results show that the ART2+GKA is significantly better than the ART2+K-means, both for mean within cluster variations and misclassification rate. A real-world problem, a recommendation agent system for a Web PDA company, is investigated. In this system, the browsing paths are used for clustering in order to analyze the browsing preferences of customers. These results also show that, based on the mean within-cluster variations, ART2+GKA is much more effective.