A fuzzy spatial data mining technique has been developed to extract relationships describing relative position of classes of objects from raster images. Several different rule forms are described which represent different types of directional relationships between classes of objects. The method has been tested with hand-generated, synthetic, and sonar imagery.

Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of convention...

Spatial Data Mining is the process of finding hidden patterns in a large spatial data set. It can be defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data . Spatial data refers to the data that pertains location and spatial dimensions of geographical entities. Some objects have spatial attributes such as positions or areas. An example...