Application of Mixtures of Gaussians for Tracking Clusters in Spatio-temporal Data
Benjamin Ertl, Jörg Meyer, Achim Streit, Matthias F. Schneider · 2019
Clustering data based on their spatial and temporal similarity has become a research area with increasing popularity in the field of data mining and data analysis. However, most clustering models for spatio-temporal data introduce additional complexity to the clustering process as well as scalability becomes a significant issue for the analysis. This article proposes a data-driven approach for tracking clusters with changing properties over time and space. The proposed method extracts cluster features based on Gaussian mixture models and tracks their spatial and temporal changes without incorporating them into the clustering process. This approach allows the application of different methods for comparing and tracking similar and changing cluster properties. We provide verification and runtime analysis on a synthetic dataset and experimental evaluation on a climatology dataset of satellite observations demonstrating a performant method to track clusters with changing spatio-temporal features.