Mining Interest Association Rules in Website Based on Hidden Markov Model

摘要

Generally, a user will access a website with a certain interest. Mining web users' interest access patterns has been an important research direction in web usage mining. These patterns are a kind of the special interest association rules essentially. In this paper, we propose a new approach for mining such rules based on Hidden Markov Model (HMM). In our approach, pages' contents and web server's log need to be preprocessed firstly. Next we present some definitions of users' access interest in a website. In addition, a new incremental algorithm Hmm_R is given to discover the interest association rules. Finally, we report on experiments conducted with simulative and real data and then testify that the algorithm can find all interest association rules efficiently.