Clustering Web Users Based on K-means Algorithm for Reducing Time Access Cost

Abstract

Numerous organizations are providing web-based services due to the consistent increase in web development and number of available web searching tools. However, the advancements in web-based services are associated with increasing difficulties in information retrieval. Efforts are now toward reducing the Internet traffic load and the cost of user access to important information. Web clustering as an important web usage mining (WUM) task groups web users based on their browsing patterns to ensure the provision of a useful knowledge of personalized web services. Based on the web structure, each Uniform Resource Locator (URL) in the web log data is parsed into tokens which are uniquely identified for URLs classification. The collective sequence of URLs a user navigated over a period of 30 minutes is considered as a session and the session is a representation of the users' navigation pattern. In this paper, K-Means algorithm was used to cluster web users based on their similarity in a vector matrix and K-means algorithm implemented several times when k=2,3,4 till k=8 and the results showed the best similarity was when k=8 and the Residual Sum of Squares (RSS) evaluation measure achieved a high intra-cluster similarity value (3.049) when k=8.