Extended algorithm for spatial characterization and discrimination rules

Spatial data mining, i.e., mining knowledge from large amounts of spatial data, is a demanding field since huge amounts of spatial data have been collected in various applications, ranging from remote sensing to geographical information systems (GIS), computer cartography, environmental assessment and planning. The collected data far exceed people's ability to analyze it. The number and the size of spatial databases are rapidly growing which results in an increasing need for spatial data mining. In this paper, we have presented a new spatial data mining algorithm for spatial characterization and discrimination rules. For spatial data mining algorithm, it is important that class membership of a database object is not only determined by its non-spatial attributes but also by the attributes of objects in its neighborhood. We have implemented the algorithm within a general framework for spatial data mining providing a small set of database primitives on top of a commercial spatial database management system. At the end, performance analysis using a real geographic database demonstrates the effectiveness of the proposed algorithms.