Discovery of patterns in spatio-temporal data using clustering techniques

Spatial-temporal clustering is very useful unsupervised learning technique and can be used to identify interesting distribution patterns from geo-located data. It is one of the most commonly used data mining techniques in many application domains, e.g. geographic information science, health science, and environmental science. In this paper, we propose a density-based spatial-temporal clustering algorithm for geo-located data points, based on an extension of the SNN (Shared Nearest Neighbor) clustering. The proposed algorithm allows the integration of location, time and other semantic attributes in the clustering process. This algorithm can find clusters of different sizes, shapes, and densities in noisy data. We evaluate the effectiveness of our algorithm through a case study involving a New York City taxi cab pickup data and Maryland crime data. The experimental results show that the proposed algorithm can discover interesting patterns and useful information from spatial-temporal data.