Clickstream Intelligent Clustering using Accelerated Ant Colony Algorithm

Abstract

Web Mining is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the Worldwide Web. There are three knowledge discovery domains that pertain to web mining: Web Content Mining, Web Structure Mining, and Web Usage Mining. Web usage mining is the process of extracting interesting patterns from web access logs. Categorizing visitors based on their interactions with a website is a key problem in Web usage mining. The clickstreams generated by various users often follow distinct patterns, the knowledge of which may help in providing customized content. In this paper, we focus on clickstream clustering based on their navigation behavior and the time spent at each page and we propose an accelerated ant based clustering algorithm (ACCANTCLUST) which is based on chemical recognition system of ants and this algorithm finds the number of clusters automatically. A comparative analysis is performed with ant colony clustering algorithm (ANTCLUST) by taking different session data sets of a Web site. Empirical results clearly show that the proposed ACCANTCLUST performs well when compared ANTCLUST.