  A novel method to analyse the spatial distribution of neutrino candidates
recorded with the ANTARES neutrino telescope is introduced, searching for an
excess of neutrinos in a region of arbitrary size and shape from any direction
in the sky. Techniques originating from the domains of machine learning,
pattern recognition and image processing are used to purify the sample of
neutrino candidates and for the analysis of the obtained skymap. In contrast to
a dedicated search for a specific neutrino emission model, this approach is
sensitive to a wide range of possible morphologies of potential sources of
high-energy neutrino emission. The application of these methods to ANTARES data
yields a large-scale excess with a post-trial significance of 2.5$\sigma$.
Applied to public data from IceCube in its IC40 configuration, an excess
consistent with the results from ANTARES is observed with a post-trial
significance of 2.1$\sigma$.
