Marco Ramoni, The Open University; Paola Sebastiani, Imperial College; Paul Cohen, University of Massachusetts
We present a Bayesian clustering algorithm for multivariate time series. A clustering is represented 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 batch of multivariate time series of propositions produced by a mobile robot perceptual system.