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

Neural networks and genetic algorithms are useful for clustering analysis in data mining. Artificial neural networks (ANNs) and genetic algorithms (GAs) have been applied in many areas with very promising results. Thus, this study uses adaptive resonance theory 2 (ART2) neural network to determine an initial solution, and then applies genetic K-means algorithm (GKA) to find the final solution for analyzing Web browsing paths in electronic commerce (EC). The proposed method is compared with ART2 followed by K-means. 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.