Bayesian Clustering by Dynamics

Authors:
Marco Ramoni;Paola Sebastiani;Paul Cohen
Affiliations:
Children's Hospital Informatics Program, Harvard Medical School, Boston, MA 02115, USA. marco_ramoni@harvard.edu;Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003, USA. sebas@math.umass.edu;Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA. cohen@cs.umass.edu
Venue:
Machine Learning - Special issue: Unsupervised learning
Year:
2002

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Abstract

This paper introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing different dynamics. To increase efficiency, the method uses an entropy-based heuristic search strategy. A controlled experiment suggests that the method is very accurate when applied to artificial time series in a broad range of conditions and, when applied to clustering sensor data from mobile robots, it produces clusters that are meaningful in the domain of application.