Robards, Matthew; Sunehag, Peter
Subsequence clustering aims to find patterns that appear repeatedly in time series data. We introduce a novel subsequence clustering technique that we call semi-Markov kmeans clustering. The clustering results in ideal examples of the repeating patterns and in labeled segmentations that can be used as training data for sophisticated discriminative methods like max-margin semi-Markov models. We are applying the new clustering technique to activity recognition from body-worn sensors by showing...[Show more]
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