Two-level Prediction Model using k-means clustering for Web Browsers Usage

Due to the popularity of World Wide Web, many organizations have changed the way of doing business, which supplements the quick development of E-commerce directly and makes the development of web usage mining expertise crucial. Predicting of user’s visiting behavior is an important technology of E- commerce application. The prediction results can be used for personalization, building proper websites, improving marketing strategy, promotion, product supply, getting marketing information, predicting market trends, and increasing the competitive strength of organizations etc. Markov model has been used for studying and understanding stochastic processes, and well suited for modeling and predicting a user’s browsing behavior on a web at category level. Bayesian theorem can be used to infer users’ browsing behaviors at webpage level. In this paper, we use the k-means 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 differences in user’s behavior. The experiments will show that our model has higher hit ratio for prediction.