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Finding temporal patterns by data decomposition

We present a new unsupervised learning technique for the discovery of temporal clusters in large data sets. Our method performs hierarchical decomposition of the data to find structure at many levels of detail and to reduce the overall computational cost of pattern discovery. We present a comparison to related methods on synthetic data sets and on real gestural and pedestrian flow data.