The rapid developments in the availability and access to spatially referenced information in a variety
of areas, has induced the need for better analysis techniques to understand the various phenomena. In
particular, spatial clustering algorithms, which group similar spatial objects into classes, can be used
for the identification of areas sharing common characteristics. The aim of this chapter is to present a
density based algorithm for the discovery of clusters of units in large spatial data sets (MDBSCAN). This
algorithm is a modification of the DBSCAN algorithm (see Ester (1996)). The modifications regard the
consideration of spatial and non spatial variables and the use of a Lagrange-Chebychev metrics instead of
the usual Euclidean one. The applications concern a synthetic data set and a data set of satellite images.
