Spatial Clustering for Data Mining with Genetic Algorithms
Vladimir Estivill‐Castro · 1997
Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. The identification of clusters in spatially referenced data provides a means of generalization of the spatial component of the data associated with a Geographical Information System. A variety of clustering formulations exists. A non-hierarchical approach in Data-mining applications is to use a medoid based version. This approach has robust behavior with respect to outliers and many heuristics have been developed that find near optimal partitions. This paper develops a genetic search heuristic for solving medoid based clustering problems. We base our genetic recombination upon Random Assorting Recombination. A comparison is made with previous solution approaches. Results show improvements on the genetic search heuristic. Keywords: Data Mining, Spatial data sets, Genetic Algorithms, Clustering. 2 Estivill-Castro & Murray 1 Introduction Geographical In...