Graph based Approach for Mining Frequent Sequential Access Patterns of Web pages

Abstract

The Internet has impacted almost every aspect of our society. Since the number of web sites and web pages has grown rapidly, discovering and understanding web users’ surfing behavior are essential for the development of successful web monitoring and recommendation systems. To capture users’ web access behavior, one promising approach is web usage mining which discovers interesting and frequent user access patterns from web logs. Sequential Web page Access pattern mining has been a focused theme in data mining research for over a decade with wide range of applications. The aim of discovering frequent sequential access (usage) patterns in Web log data is to obtain information about the navigational behavior of the users. This can be used for advertising purposes, for creating dynamic user profiles etc. We propose a new approach for mining the web usage data by creating graph using web access sequence of sorted web log and mining the useful sequential access pattern.