Clustering web users for reductions the internet traffic load and users access cost based on K-means algorithm

Abstract

The continuous growth in the size and use of the Internet is increasing the difficulties in searching for information. Reductions on the Internet traffic load and user access cost is therefore particular important. Clustering is an important part of web mining that involves finding natural groupings of web resources or web users. Researchers have pointed out some important differences between clustering in conventional applications and clustering in web mining. 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. This paper proposes a variation of the K-means clustering algorithm based on properties of rough sets. The proposed algorithm represents the clustering of the web users based on their browsing activities or patterns on the web. Specifically, a user may visit a website often and spends much time on each visit. users with similar browsing activities are clustered or grouped in to clusters. The paper also describes the design of an experiment including data collection and the clustering process.