Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate a proximity matching problem among clusters and features. The investigation involves proximity relationship measurement between clusters and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. An efficient algorithm, for solving the problem, is proposed and evaluated. The algorithm applies a standard multi-step paradigm in combining with novel lower and upper proximity bounds. The algorithm is implemented in several different modes. Our experiment results do not only give a comparison among them but also illustrate the efficiency of the algorithm.
Lecture notes in computer science: Advances in Spatial Databases: proceedings of the 6th International Symposium on Spatial Databases (SSD '99) Hong Kong, China, 20-23 July 1999 / Ralf Hartmut Guting, Dimitris Papadias and Fred Lochovsky (eds.),
Vol. 1651, pp. 188-206