Affiliations: Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan | Computational Intelligence Group, University of the Basque Country, UPV/EHU, Spain
Abstract: We describe a web access log analysis of an e-commerce site as visualized by FACT-Graph with a sequential probability ratio test. Web access log analysis is an important task, and is termed as web usage mining. A variety of studies have been conducted on this subject. However, there is no known study that has focused on understanding the trends and relationships in the analysis considering the structural change of the access trends, especially in visualization fields. To solve this problem, we propose an analysis using FACT-Graph with a sequential probability ratio test. FACT-Graph has been used for the trend visualization of text mining, while the sequential probability ratio test has been used for quality control. We utilize the sequential probability test to detect structural changes and FACT-Graph for visualization by considering the session and accessed pages in the access log as articles and words in text. We can visualize data by using FACT-Graph in an experiment using 1.6 million access logs generated between July 2010 and June 2011 on the basis of 13 structural change points detected by the sequential probability ratio test.
Keywords: Web usage mining, visualization, FACT-graph, sequential probability ratio test, access log
