Spatiotemporal k-means

arXiv:2211.05337v2 Announce Type: replace
Abstract: 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 …

abstract acquisition application arxiv behavior clustering cs.lg data discovery human identity k-means moving object objects patterns sensor supervision technologies trends type