Recent research initiatives have addressed the need for improved performance of Web page prediction that would profit many applications, e-business in particular. The most widely used techniques for this purpose are Markov model, association rules and clustering. Implementing pattern discovery techniques as such helps predict the next page to be accessed a Web user based on the user's previous browsing patterns. However, each of the aforementioned techniques has its own limitations, especially when it comes to accuracy and space complexity. This paper provides an improved prediction accuracy and state space complexity by using novel approaches that combine clustering, association rules and Markov models. The three techniques are integrated together to maximize their strengths. The integration model has been shown to achieve better prediction accuracy than individual and other integrated models.

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