Extraction of spatial proximity patterns by concept generalization

We study the spatial data mining problem of how to extract a special type of proximity relationship-namely that of distinguishing two clusters of points based on the types of their neighbouring features. The points in the clusters may represent houses on a map, and the features may represent spatial entities such as schools, parks, golf courses, etc. Classes of features are organized into concept hierarchies. We develop algorithm GenDis which uses concept general-ization to identify the distinguishing features or concepts which serve as discriminators. Further-more, we study the issue of which discriminators axe “better ” than others by introducing the no-tion of maximal discriminators, and by using a ranking system to quantitatively weigh maximal discriminators from different concept hierarchies