Enhanced Initialization and Distance function for Pattern discovery in Web Usage Mining

Abstract

            Web mining is a discipline of datamining that focus on WWW for itsprimary source of information that includes all of its components. It uses datalogging techniques and algorithms to reveal all hidden facts directly from net documents, hyperlinks and server logs. It collects and analyzes the records in order to gain insight into user behavior. The categories are Content, Structure and Usage mining where the content extracts useful info of documents, Structure processing discovers structural information from pages and the final category identifies interesting usage patterns from navigation history of user stored in weblogs. Among this, the later comprises of three phases: Preprocessing, Pattern discovery and analysis. This paper applies method for Pattern discovery in Usage Mining with classification technique. This research put forth an enhanced procedure to upgrade existing algorithm K-Nearest Neighbor (KNN). The primary objective is to get User identification and HTTP Status code Categorization for a specific university in order to improve site. Each user is identified and classified according to IP address specified in the log file. The work is implemented in Rapid miner tool and assessed with suitable evaluation metrics.