摘要: This paper introduces a Bayesian method for clustering dynamic processes. The models dynamics as Markov chains and then applies an agglomerative procedure to discover the most probable set of clusters capturing different dynamics. To increase efficiency, uses entropy-based heuristic search strategy. A controlled experiment suggests that is very accurate when applied artificial time series in broad range conditions and, sensor data from mobile robots, it produces are meaningful domain application.
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