  Spatiotemporal data is increasingly available due to emerging sensor and data
acquisition technologies that track moving objects. Spatiotemporal clustering
addresses the need to efficiently discover patterns and trends in moving object
behavior without human supervision. One application of interest is the
discovery of moving clusters, where clusters have a static identity, but their
location and content can change over time. We propose a two phase
spatiotemporal clustering method called spatiotemporal k-means (STkM) that is
able to analyze the multi-scale relationships within spatiotemporal data. By
optimizing an objective function that is unified over space and time, the
method can track dynamic clusters at both short and long timescales with
minimal parameter tuning and no post-processing. We begin by proposing a
theoretical generating model for spatiotemporal data and prove the efficacy of
STkM in this setting. We then evaluate STkM on a recently developed collective
animal behavior benchmark dataset and show that STkM outperforms baseline
methods in the low-data limit, which is a critical regime of consideration in
many emerging applications. Finally, we showcase how STkM can be extended to
more complex machine learning tasks, particularly unsupervised region of
interest detection and tracking in videos.
