Discovering associations in spatial data — An efficient medoid based approach

Abstract

Spatial data mining is the discovery of novel and interesting relationships and characteristics that may exist implicitly in spatial databases. The identification of clusters coupled with Geographical Information System provides a means of information generalization. A variety of clustering approaches exists. A non-hierarchical method in data mining applications is the medoid approach. Many heuristics have been developed for this approach. This paper carefully analyses the complexity of hill-climbing heuristics for medoid based spatial clustering. Improvements to recently suggested heuristics like CLARANS are identified. We propose a novel idea, the stopping early of the heuristic search, and demonstrate that this provides large savings in computational time while the quality of the partition remains unaffected.

This research was supported in part by a grant from the Australian Research Council.