Algorithm of Web Session Clustering Based on Increase of Similarities

The task of Web session clustering is to group Web sessions according to similarities, so as to maximize the similarities within the group and, minimize the similarities between the groups. The number of clusters, the initial data points of the respective clusters, and the defining of criterion function are the 3 key points and difficulties that deserve consideration in Web session clustering. WSCBIS, Web session clustering based on increase of similarities, defines the number of clusters according to the knowledge of application fields; it takes advantage of ROCK to decide the initial data points of each cluster; it also determines the criterion function according to the contributions of overall increase in similarities made by dividing Web sessions into different clusters - which not only overcomes the shortcomings of traditional clustering algorithm which merely focus on partial similarities, but also decreases the complexities of time and space.