Multivariate Clustering by Dynamics

We present a Bayesian clustering algorithm for multivariate time series. A clustering is regarded as a probabilistic model in which the unknown auto-correlation structure of a time series is approximated by a first order Markov Chain and the overall joint distribution of the variables is simplified by conditional independence assumptions. The algorithm searches for the most probable set of clusters given the data using a entropy-based heuristic search method. The algorithm is evaluated on a set of multivariate time series of propositions produced by the perceptual system of a mobile robot.

1. Knowledge Media Institute, The Open University

2. Department of Mathematics, Imperial College of Science, Technology and Medicine

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

Publication(s)

Also in Proceedings of the 2000 National Conference on Artificial Intelligence (AAAI-2000), Morgan Kaufman, San Mateo, CA, 2000.