Spatial data involves very large datasets. Mining implicit relationships among different attributes or bands of spatial data can be prohibitively time consuming with traditional methods.
Data mining includes association rule mining, classification and clustering.
Association rule mining, originally proposed for market basket data, now is applied in many areas. Association rule mining is very promising for spatial data. Classification and clustering techniques abound but their application to huge datasets has been problematic. Huge amounts of spatial data have been collected in various ways, but the discovery of the knowledge contained therein has just begun. New spatial data-mining algorithms for association rule mining, classification, and clustering result directly from this new model.