A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models

Abstract

This paper presents clustering techniques that partition temporal data into homogeneous groups, and constructs state based proles for each group in the hidden Markov model (HMM) framework. We propose a Bayesian HMM clustering methodology that improves upon existing HMM clustering by incorporating HMM model size selection into clustering control structure to… (More)

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@inproceedings{Li2000ABA, title={A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models}, author={Cen Li and Gautam Biswas}, booktitle={ICML}, year={2000} }