Integrating Markov Model with clustering for predicting web page accesses

Abstract

[Abstract]: Predicting the next page to be accessed by Web users has attracted a large amount of research work lately due to the positive impact of such prediction on different areas of Web based applications. One major technique applied for this intention is Markov model. Low
\norder Markov models are coupled with low accuracy, whereas high order Markov models are associated with high state space complexity. This paper involves incorporating clustering techniques by dividing pre-processed data into meaningful clusters then performing low order Markov models to each cluster instead of the whole data sets.
\nDifferent distance measures of k-means clustering algorithm are examined in order to find an optimal one. Experiments reveal that clustering of Web documents according to Web services improves the low order Markov models accuracy.
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