Robards, MatthewSunehag, Peter2015-12-10December 69781424452422http://hdl.handle.net/1885/58236Subsequence 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 how it can enable a system to learn from data that is only annotated by an ordered list of activity types that have been undertaken. This kind of annotation, unlike a detailed segmentation of the sensor data, is easily provided by a non-expert user. We show that we can achieve equally good results using only an ordered list of activity types for training as when using a full detailed labeled segmentation.Keywords: Activity recognition; Body-worn sensors; Clustering results; Clustering techniques; Discriminative methods; Expert users; K-means clustering; Semi Markov model; Semi-Markov; Sensor data; Time-series data; Training data; Clustering algorithms; Markov proce Activity recognition; Clustering; Subsequence; Time-series200910.1109/ICDM.2009.132016-02-24