On Clustering Users' Behaviors in Video Sessions

We study the extraction of characteristics of user behavior in video session encoded as stochastic matrices of finite Markov chain. These behaviors are clustered using a dissimilarity based on the Kullbach-Leibler divergence between probability distributions. The center of each cluster is regarded as the model that generates the behaviors assigned to the cluster. This choice is based on the relationship that we establish between the dissimilarity between the behavior and the model, and the probability that the model generates the behavior. Experimental results that evaluate the quality of the clustering validate our choice of the models.

• Type: Conference
• Year: 2007
• Where: DMIN
• Authors: Sylvain Mongy, Chabane Djeraba, Dan A. Simovici
• Keywords: Behavior, Data Mining, DMIN 2007, User Behavior, finite Markov Chain