Analysis of Browsing Behaviors with Ant Colony Clustering Algorithm

Abstract

The characteristics of users' browsing behaviors on websites can be used to analyze system performance as well as network communication, understand users’ reaction and motivation, and build adaptive websites. However, the motivation, requirement and experience of users may dynamically change, which cause difficulty in exactly refining a stable behavior pattern and describing their shifted interest. This paper introduces an optimized ant colony clustering algorithm (OACA) in dynamic pattern discovery, and explores the structured formula to describe the users’ browsing behavior patterns as well as to analyze their characteristics adaptively. The test and results show that users are clustered accurately based on their similar browsing behavior from dynamic Web log data.