  We develop a new analysis approach towards identifying related radio
components and their corresponding infrared host galaxy based on unsupervised
machine learning methods. By exploiting PINK, a self-organising map algorithm,
we are able to associate radio and infrared sources without the a priori
requirement of training labels. We present an example of this method using
$894,415$ images from the FIRST and WISE surveys centred towards positions
described by the FIRST catalogue. We produce a set of catalogues that
complement FIRST and describe 802,646 objects, including their radio components
and their corresponding AllWISE infrared host galaxy. Using these data products
we (i) demonstrate the ability to identify objects with rare and unique radio
morphologies (e.g. 'X'-shaped galaxies, hybrid FR-I/FR-II morphologies), (ii)
can identify the potentially resolved radio components that are associated with
a single infrared host and (iii) introduce a "curliness" statistic to search
for bent and disturbed radio morphologies, and (iv) extract a set of 17 giant
radio galaxies between 700-1100 kpc. As we require no training labels, our
method can be applied to any radio-continuum survey, provided a sufficiently
representative SOM can be trained.
