HMM Based On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
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The objective of this paper is to propose an approach that extracts automatically web user profiling based on user navigation paths. Web user profiling consists of the best representative behaviors, represented by Markov models (MM). To achieve this objective, our approach is articulated around three notions: (1) Applying probabilistic exploration using Markov models. (2) Avoiding the problem of Markov model high-dimensionality and sparsity by clustering web documents, based on their content, before applying the Markov analysis. (3) Clustering Markov models, and extraction of their gravity centers. On the basis of these three notions, the approach makes possible the prediction of future states to be visited in k steps and navigation sessions monitoring, based on both content and traversed paths. The original application of the approach concerns the exploitation of multimedia archives in the perspective of the Copyright Deposit that preserves French's WWW documents. The approach may be the exploitation tool for any web site.