Web Usage Mining: An Implementation

Abstract

Web usage mining is the area of data mining which deals with the discovery and analysis of usage patterns from Web data, specifically web logs, in order to improve web based
applications. Web usage mining consists of three phases, preprocessing, pattern discovery,and pattern analysis. After the completion of these three phases the user can find the required usage patterns and use these information for the specific needs. In this project, the DSpace log files have been preprocessed to convert the data stored in
them into a structured format. Thereafter, the general procedures for bot-removal and session-identification from a web log file, have been written down with certain
modifications pertaining to the DSpace log files, in an algorithmic form. Furthermore, analysis of these log files using a subjective interpretation of a recently proposed algorithm EIN-WUM has also been conducted. This algorithm is based on the artificial immune system model and uses this model to learn and extract information present in the web data i.e server logs. This algorithm has been duly modified according to DSpace@NITR Website structure.