Bayesian Clustering by Dynamics

Authors

Marco Ramoni, Harvard University
Paola Sebastiani, University of Massachusetts - Amherst
Paul Cohen, University of Massachusetts - Amherst

Publication Date

2001

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 ef£ciency, 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.

Comments

This paper was harvested from CiteSeer

Recommended Citation

Ramoni, Marco; Sebastiani, Paola; and Cohen, Paul, "Bayesian Clustering by Dynamics" (2001). Computer Science Department Faculty Publication Series. 158.
Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/158