Cluster analysis has been pursued from a number of directions for identifyi
ng interesting relationships and patterns in spatial information. a major e
mphasis is currently on the development and refinement of optimization-base
d clustering models for the methods of exploring spatially referenced data.
within this context, two basic methods exist for identifying clusters that
are most similar. An interesting feature of these two approaches is that o
ne method approximates the relationships inherent in the other method. This
is significant given that the approximation approach is invariably utilize
d for cluster detection important and aspatial analysis. A number of spatia
l applications are investigated which highlight the differences in clusters
produced by each model This is an important contribution because the diffe
rences are in fact quite significant, yet these contrasts are not widely kn
own or acknowledged.