  Data mining techniques, including clustering and classification tasks, for
the automatic information extraction from large datasets are increasingly
demanded in several scientific fields. In particular, in the astrophysical
field, large archives and digital sky surveys with dimensions of 10E12 bytes
currently exist, while in the near future they will reach sizes of the order of
10E15. In this work we propose a multidimensional indexing method to
efficiently query and mine large astrophysical datasets. A novelty detection
algorithm, based on the Support Vector Clustering and using density and
neighborhood information stored in the index structure, is proposed to find
regions of interest in data characterized by isotropic noise. We show an
application of this method for the detection of point sources from a gamma-ray
photon list.
