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Clustering objects into synthetic groups is a natural activity of anyscience. Astrophysics is not an exception and is now facing a deluge of data.For galaxies, the one-century old Hubble classification and the Hubble tuningfork are still largely in use, together with numerous mono-or bivariateclassifications most often made by eye. However, a classification must bedriven by the data, and sophisticated multivariate statistical tools are usedmore and more often. In this paper we review these different approaches inorder to situate them in the general context of unsupervised and supervisedlearning. We insist on the astrophysical outcomes of these studies to show thatmultivariate analyses provide an obvious path toward a renewal of ourclassification of galaxies and are invaluable tools to investigate the physicsand evolution of galaxies.

Multivariate Approaches to Classification in Extragalactic Astronomy