A probabilistic validation algorithm for Web users' clusters
George Pallis, Lefteris Angelis, Athena I. Vakali, Jaroslav Pokorný · 2005
Cluster analysis is one of the most important aspects in the data mining process for discovering groups and identifying interesting distributions or patterns over the considered data sets. In the context of Web data mining, model-based clustering algorithms are often used to cluster similar users' sessions in order to determine Website access behaviors. An important issue in cluster analysis is the evaluation of clustering results to find the partitioning that best fits the underlying data. In this paper, we present a novel validation technique for model based clustering approaches.