International journal of computer science & information Technology (IJCSIT) Vol.2, No.4, August 2010 DOI : 10.5121/ijcsit.2010.2406 60 Efficient Web Log Mining Using Enhanced Apriori Algorithm with Hash Tree and Fuzzy S.Veeramalai 1 , N.Jaisankar 2 and A.Kannan 3 Department of Information Science and Technology, Anna University –Chennai Chennai-600025, Tamil Nadu, India 1 veera2000uk@gmail.com , 2 jaisasi_win@yahoo.com , 3 kannan@annauniv.edu Abstract Web usage mining is the type of Web mining activity that involves the automatic discovery of user access patterns from one or more Web servers. In this paper we analyze the pattern using different algorithms like Apriori, Hash tree and Fuzzy and then we used enhanced Apriori algorithm to give the solution for Crisp Boundry problem with higher optimized efficiency while comparing to other algorithms. Keyword Data mining, Web mining, Web log, Association rule, Apriori, Fuzzy. 1. Introduction The aim in web mining is to discover and retrieve useful and interesting patterns from a large dataset. In web mining, this dataset is the huge web data [7]. Web data contains different kinds of information, including, web structure data, web log data, and user profiles data [9, 10]. Web mining is the application of data mining techniques to extract knowledge from web data, where at least one of structure or usage data is used in the mining process. Web usage mining has various application areas such as web pre-fetching, link prediction, site reorganization and web personalization [1, 2, and 14]. Most important phases of web usage mining are the [2,3] reconstruction of user sessions by using heuristics techniques and discovering useful patterns from these sessions by using pattern discovery techniques like association rule mining, Apriori etc [4,3]. We propose an integrated system (Web Tool) for applying data mining [16] techniques such as association rules or sequential patterns on access log files. The fig.1 represents the System architecture diagram for our paper.