Spatiotemporal k-means
This addresses the need for efficient unsupervised pattern discovery in spatiotemporal data, such as tracking moving clusters in applications like animal behavior analysis, but it appears incremental as it builds on existing clustering methods.
The authors tackled the problem of discovering patterns in moving object behavior by proposing spatiotemporal k-means (STkM), a two-phase clustering method that optimizes a unified objective function over space and time, and showed it outperforms baseline methods in the low-data limit on a collective animal behavior benchmark dataset.
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.