  The growth of the World Wide Web has emphasized the need for improvement in
user latency. One of the techniques that are used for improving user latency is
Caching and another is Web Prefetching. Approaches that bank solely on caching
offer limited performance improvement because it is difficult for caching to
handle the large number of increasingly diverse files. Studies have been
conducted on prefetching models based on decision trees, Markov chains, and
path analysis. However, the increased uses of dynamic pages, frequent changes
in site structure and user access patterns have limited the efficacy of these
static techniques. In this paper, we have proposed a methodology to cluster
related pages into different categories based on the access patterns.
Additionally we use page ranking to build up our prediction model at the
initial stages when users haven't already started sending requests. This way we
have tried to overcome the problems of maintaining huge databases which is
needed in case of log based techniques.
