Abstract
Spatial data mining (SDM) is searching important relationships and characteristics that can clearly exist in spatial databases. This content aims to compare object clustering algorithms for spatial data mining, before identifying the most efficient algorithm. To this end, this paper compare k-means, Partionning Around Medoids (PAM) and Clustering Large Applications based on RANdomized Search (CLARANS) algorithms based on computing time. Experimental results indicate that, CLARANS is very efficient and effective.
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