Soft clustering analysis of galaxy morphologies: A worked example with
  SDSS
  Context: The huge and still rapidly growing amount of galaxies in modern sky
surveys raises the need of an automated and objective classification method.
Unsupervised learning algorithms are of particular interest, since they
discover classes automatically. Aims: We briefly discuss the pitfalls of
oversimplified classification methods and outline an alternative approach
called "clustering analysis". Methods: We categorise different classification
methods according to their capabilities. Based on this categorisation, we
present a probabilistic classification algorithm that automatically detects the
optimal classes preferred by the data. We explore the reliability of this
algorithm in systematic tests. Using a small sample of bright galaxies from the
SDSS, we demonstrate the performance of this algorithm in practice. We are able
to disentangle the problems of classification and parametrisation of galaxy
morphologies in this case. Results: We give physical arguments that a
probabilistic classification scheme is necessary. The algorithm we present
produces reasonable morphological classes and object-to-class assignments
without any prior assumptions. Conclusions: There are sophisticated automated
classification algorithms that meet all necessary requirements, but a lot of
work is still needed on the interpretation of the results.
