Abstract:
Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for the automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns. In this paper we describe the ongoing spatial data mining research by the Spatial Database Research Group, University of Minnesota. We discuss several computationally efficient and scalable techniques for analyzing large geospatial data sets and their applications in location prediction, spatial outliers detection and co-location association rules mining.
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