Efficiently matching proximity relationships in spatial databases

Abstract

Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather fore- casting, 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.