Web Usage Mining Based on Clustering of Browsing Features
Chu-Hui Lee, Yu-Hsiang Fu · 2008
Predicting of user's browsing behavior is an important technology of E-commerce application. The prediction results can be used for personalization, building proper Web site, improving marketing strategy, promotion, product supply, getting marketing information, forecasting market trends, and increasing the competitive strength of enterprises etc. In this paper, we use the hierarchical agglomerative clustering to cluster users' browsing behaviors. The prediction results by two levels of prediction model framework work well in general cases. However, two levels of prediction model suffer from the heterogeneity user's behavior. In this paper, we will improve two levels of prediction model to achieve higher hit ratio.