Tech Report

Bayesian Clustering by Dynamics

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.  An experiment suggests that the method is very accurate when applied to artificial time series in a broad range of conditions.  When the method is applied to clustering simulated military engagements and sensor data from mobile robots, it produces clusters that are meaningful in the domains of application.

1. Knowledge Media Institute, The Open University

2. Department of  Statistics, The Open University.

3. Department of Computer Science, University of Massachusetts at Amherst.

ID: kmi-99-05

Date: 1999

Author(s): Marco Ramoni, Paola Sebastiani, Paul Cohen, John Warwick and James Davis

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