Modern photometric multiband digital surveys produce large amounts of data
that, in order to be effectively exploited, need automatic tools capable to
extract from photometric data an objective classification. We present here a
new method for classifying objects in large multi-parametric photometric data
bases, consisting of a combination of a clustering algorithm and a cluster
agglomeration tool. The generalization capabilities and the potentialities of
this approach are tested against the complexity of the Sloan Digital Sky Survey
archive, for which an example of application is reported.Comment: To appear in the Proceedings of the "1st Workshop of Astronomy and
Astrophysics for Students" - Naples, 19-20 April 200
