The field of Movement Ecology is experiencing a period of rapid growth in availability of data, and like many other fields is turning to data science for tools and methods to cope with the new challenges and opportunities that this presents. One rich and interesting source of data is the bio-logger. These small electronic devices are attached to animals free to roam in their natural habitats, and report back readings from multiple sensors, including GPS and accelerometer bursts. A common use of this accelerometer data is for supervised learning of behavioral modes. However, there is a need for unsupervised analysis tools as well, due to the inherent difficulties of obtaining a labeled dataset, which in some cases is either infeasible or does not successfully encompass the full repertoire of behavioral modes of interest. Here we present a matrix factorization based clustering method that allows either a soft or a hard partitioning of acceleration measurements, as well as a straight-forward way of drawing insight into the complex movements themselves. The method is validated by comparing the partitions with a labeled dataset, and is further compared to standard methods highlighting the advantages of the new method.