  Aims. A new method is applied to the segmentation, and further analysis of
the outliers resulting from the classification of astronomical objects in large
databases is discussed. The method is being used in the framework of the Gaia
satellite DPAC (Data Processing and Analysis Consortium) activities to prepare
automated software tools that will be used to derive basic astrophysical
information that is to be included in Gaia final archive. Methods. Our
algorithm has been tested by means of simulated Gaia spectrophotometry, which
is based on SDSS observations and theoretical spectral libraries covering a
wide sample of astronomical objects. Self-Organizing Maps (SOM) networks are
used to organize the information in clusters of objects, as homogeneous as
possible, according to their spectral energy distributions (SED), and to
project them onto a 2-D grid where the data structure can be visualized.
Results. We demonstrate the usefulness of the method by analyzing the spectra
that were rejected by the SDSS spectroscopic classification pipeline and thus
classified as "UNKNOWN". Firstly, our method can help to distinguish between
astrophysical objects and instrumental artifacts. Additionally, the application
of our algorithm to SDSS objects of unknown nature has allowed us to identify
classes of objects of similar astrophysical nature. In addition, the method
allows for the potential discovery of hundreds of novel objects, such as white
dwarfs and quasars. Therefore, the proposed method is shown to be very
promising for data exploration and knowledge discovery in very large
astronomical databases, such as the upcoming Gaia mission.
