An Efficient and Accurate Discovery of Frequent Patterns Using Improved WARM to Handle Large Web Log Data

Abstract

In the booming era of Internet, web search is inevitable to everyone. In web search, mining frequent pattern is a challenging one, particularly when handling tera byte size databases. Finding solution for these issues have primarily started attracting the key researchers. Due to high the demand in finding the best search methods, it is very important and interesting to predict the user's next request. The number of frequent item sets and the database scanning time should be reduced for fast generating frequent pattern mining. It fulfills user's accurate need in a magic of time and offers a customized navigation. Association Rule mining plays key role in discovering associated web pages and many researchers are using Apriori algorithm with binary representation in this area. But it does not provide best solution for finding navigation order of web pages. To overcome this, weighted Apriori was introduced. But still, it is difficult to produce most favorable results especially in large databases. In the effort of finding best solution, the authors have proposed a novel approach which combines weighted Apriori and dynamic programming. The conducted experiments so far, shows' better tracking of maintaining navigation order and gives the confidence of making the best possible results. The proposed approach enriches the web site effectiveness, raises the knowledge in surfing, ensures prediction accuracies and achieves less complexity in computing with very large databases.