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 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.