Balancing Spectral Clustering for Segmenting Spatio-temporal Observations of Multi-agent Systems

Abstract

We examine the application of spectral clustering for breaking up the behavior of a multi-agent system in space and time into smaller, independent elements. We cluster observations of individual entities in order to identify significant changes in the parameter space (like spatial position)and detect temporal alterations of behavior within the same framework. Data is also influenced by knowledge about important events. Clusters are pre-processed at each step of the iterative subdivision to make the algorithm invariant against spatial scaling, rotation, replay speed and varying sampling frequency. A method is presented to balance spatial and temporal segmentation based on the expected group size. We demonstrate our results by analyzing the outcomes of a computer game.