User Navigation Pattern Discovery using Fast Adaptive Neuro-Fuzzy Inference System

World Wide Web is a huge repository of web pages and links. It provides abundance information for the Internet users. The growth of web is incredible as it can be seen in present days. Users’ accesses are recorded in web logs. From the user’s perspective, it is very difficult to extract useful knowledge from the huge amount of information and secondly, it is also difficult to extract for the users to access relevant information efficiently. From business point of view, the webmasters and administrators find it difficult to organize the contents of the websites to cater to the needs of the users. Both the problems can be solved if the web navigation behavior of a user can be understood. One way to extract such information is to use Web Usage Mining. Web Usage Mining consists of preprocessing, pattern discovery and pattern analysis. Preprocessing is the step which transforms the raw log file into a form that is more suitable for mining. Four steps are used in preprocessing, they are, data cleaning, user identification, session identification and formatting the result to suit the clustering algorithm. The clustering technique used in this paper is Fuzzy-Possibilistic C-Means clustering technique. In pattern analysis, this paper uses Fast Adaptive Neuro-Fuzzy Inference System (FANFIS). The experimental results suggest that the proposed technique for web log mining results in better prediction of user behaviors when compared to the existing web usage mining techniques. Keyword: Raw log file, Preprocessing, Fuzzy-Possibilistic C-Means, Web Usage Mining, Adaptive Neuro Fuzzy Interference System (ANFIS).