Mining multiple-level spatial association rules for objects with a broad boundary

Spatial data mining, i.e., mining knowledge from large amounts of spatial d ata, is a demanding field since huge amounts of spatial data have been coll ected in various applications, ranging from remote sensing to geographical information systems (GIS), computer cartography, environmental assessment a nd planning. The collected data far exceeds people's ability to analyze it. Thus, new and efficient methods are needed to discover knowledge from larg e spatial databases. Most of the spatial data mining methods do not take in to account the uncertainty of spatial information. In our work we use objec ts with broad boundaries, the concept that absorbs all the uncertainty by w hich spatial data is commonly affected and allows computations in the prese nce of uncertainty without rough simplifications of the reality. The topolo gical relations between objects with a broad boundary can be organized into a three-level concept hierarchy. We developed and implemented a method for an efficient determination of such topological relations. Based on the hie rarchy of topological relations we present a method for mining spatial asso ciation rules for objects with uncertainty. The progressive refinement appr oach is used for the optimization of the mining process. (C) 2000 Elsevier Science B.V. All rights reserved.