Spatial data mining is the discovery of interesting relationships and characteristics that 
may exist implicitly in spatial databases. To this end, this paper has three main 
contributions. First, we propose a new clustering method called CLARANS, whose aim is to 
identify spatial structures that may be present in the data. Experimental results indicate 
that when compared with existing clustering methods, CLARANS is very efficient and effective.
Second, we investigate how CLARANS can handle not only points objects, but also polygon 
objects efficiently. One of the methods considered, called the IR-approximation, is very 
efficient in clustering convex and non-convex polygon objects. Third, building on top of 
CLARANS, we develop two spatial data mining algorithms that aim to discover relationships 
between spatial and non-spatial attributes. Both algorithms can discover knowledge that is 
difficult to find with existing spatial data mining algorithms.