  We present an application of unsupervised machine learning - the
self-organised map (SOM) - as a tool for visualising, exploring and mining the
catalogues of large astronomical surveys. Self-organisation culminates in a
low-resolution representation of the 'topology' of a parameter volume, and this
can be exploited in various ways pertinent to astronomy. Using data from the
Cosmological Evolution Survey (COSMOS), we demonstrate two key astronomical
applications of the SOM: (i) object classification and selection, using the
example of galaxies with active galactic nuclei as a demonstration, and (ii)
photometric redshift estimation, illustrating how SOMs can be used as totally
empirical predictive tools. With a training set of ~3800 galaxies with
z_spec<1, we achieve photometric redshift accuracies competitive with other
(mainly template fitting) techniques that use a similar number of photometric
bands (sigma(Dz)=0.03 with a ~2% outlier rate when using u*-band to 8um
photometry). We also test the SOM as a photo-z tool using the PHoto-z Accuracy
Testing (PHAT) synthetic catalogue of Hildebrandt et al. (2010), which compares
several different photo-z codes using a common input/training set. We find that
the SOM can deliver accuracies that are competitive with many of the
established template-fitting and empirical methods. This technique is not
without clear limitations, which are discussed, but we suggest it could be a
powerful tool in the era of extremely large - 'petabyte' - databases where
efficient data-mining is a paramount concern.
