Multivariate Clustering by Dynamics

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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.
Marco Ramoni, Paola Sebastiani, Paul R. Cohen
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where AAAI
Authors Marco Ramoni, Paola Sebastiani, Paul R. Cohen
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