Tech Reports

Tech Report kmi-00-05 Abstract

Sequence Learning via Bayesian Clustering by Dynamics
Techreport ID: kmi-00-05
Date: 2000
Author(s): Paola Sebastiani, Marco Ramoni and Paul Cohen

This chapter 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 algorithm uses an entropy-based heuristic search strategy. When applied to clustering sensor data from mobile robots, the algorithm produces clusters that are meaningful in the domains of application. 1. Department of Mathematics, Imperial College of Science, Technology and Medicine 2. Knowledge Media Institute, The Open University 3. Department of Computer Science, University of Massachusetts at Amherst

Publication(s):

Also in Sequence Learning: Paradigms, Algorithms, and Applications, L. Giles and R. Sun, editors, Springer, New York, NY, 2000.