Abstract
Now-a-days, with the massive size of the web it has become hectic as well as time-consuming for the users to search for the most appropriate page in least time. Prediction of the next page saves users’ time and it becomes easy for the user to reach the most suitable or correct page. In this paper, web page prediction technique has been improved by combining clustering with markov rule and page ranking algorithm. The K-means clustering technique is used for the accumulation of the similar web pages. Page Rank Algorithm is used here to assign probabilities to web-pages from beforehand according to their importance. Markov rule has been used on each cluster to evaluate occurrences of each web pages visited under different sessions and markov model is applied to predict the next web page from the current web page. The rule of transition probability of markov model has been used to predict the next web page from the current web page.
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