Discovering and Mining User Web-page Traversal Patterns

As the popularity of WWW explodes, a massive amount of data is gathered by Web servers in the form of Web access logs. This is a rich source of information for understanding Web user surfing behavior. Web Usage Mining, also known as Web Log Mining, is an application of data mining algorithms to Web access logs to find trends and regularities in Web users' traversal patterns. The results of Web Usage Mining have been used in improving Web site design, business and marketing decision support, user profiling, and Web server system performance. In this thesis we study the application of assisted exploration of OLAP data cubes and scalable sequential pattern mining algorithms to Web log analysis. In multidimensional OLAP analysis, standard statistical measures are applied to assist the user at each step to explore the interesting parts of the cube. In addition, a scalable sequential pattern mining algorithm is developed to discover commonly traversed paths in large data sets. Our experimental and performance studies have demonstrated the effectiveness and efficiency of the algorithm in comparison to previously developed sequential pattern mining algorithms. In conclusion, some further research avenues in web usage mining are identified as well. iv Dedication To my parents v Acknowledgments I would like to thank my supervisor Dr. Jiawei Han for his support, sharing of his knowledge and the opportunities that he gave me. His dedication and perseverance has always been exemplary to me. I am also grateful to TeleLearning for getting me started in Web Log Analysis.