PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A model based appraoch to sequence clustering
Henri Binsztok, Thierry Artieres and Patrick Gallinari
In: ECAI 2004, Aug 2004, Madrid, Spain.

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Abstract

We present a Hidden Markov Model-based approach to cluster sequences. This problem is adressed in term of learning Hidden Markov Models (HMM) structure from data. Using a top-down approach, we iteratively simplify an initial HMM that consists in a mixture of as many left-right HMMs as training sequences. Our approach allows to learn, in an unsupervised manner, the cluster models that best represent training data. We provide experimental results on two different application fields, on-line handwriting signals and hypermedia navigation patterns.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Theory & Algorithms
ID Code:186
Deposited By:Thierry Artieres
Deposited On:05 June 2004

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