In this paper, we study two spatial knowledge discovery problems involving proximity 
relationships between clusters and features. The first problem is: Given a cluster of points, 
how can we efficiently find features (represented as polygons) that are closest to the 
majority of points in the cluster? We measure proximity in an aggregate sense due to the 
non-uniform distribution of points in a cluster (e.g., houses on a map), and the different 
shapes and sizes of features (e.g., natural or man-made geographic features). The second 
problem is: Given n clusters of points, how can we extract the aggregate proximity 
commonalities (i.e., features) that apply to most, if not all, of the n clusters? Regarding 
the first problem, the main contribution of the paper is the development of Algorithm CRH 
which uses geometric approximations (i.e., circles, rectangles, and convex hulls) to filter 
and select features. Highly scalable and incremental, Algorithm CRH can examine over 50,000 
features and their spatial relationships with a given cluster in approximately one second of 
CPU time. Regarding the second problem, the key contribution is the development of 
Algorithm GenCom that makes use of concept generalization to effectively derive many 
meaningful commonalities that cannot be found otherwise.