Tech Reports
Tech Report kmi-99-05 Abstract
Bayesian Clustering by Dynamics
Techreport ID: kmi-99-05
Date: 1999
Author(s): Marco Ramoni, Paola Sebastiani, Paul Cohen, John Warwick and James Davis
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.
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