  The Low Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) is a
low-frequency radio continuum survey of the Northern sky at an unparalleled
resolution and sensitivity. In order to fully exploit this huge dataset and
those produced by the Square Kilometre Array in the next decade, automated
methods in machine learning and data-mining will be increasingly essential both
for morphological classifications and for identifying optical counterparts to
the radio sources. Using self-organising maps (SOMs), a form of unsupervised
machine learning, we created a dimensionality reduction of the radio
morphologies for the $\sim$25k extended radio continuum sources in the LoTSS
first data release, which is only $\sim$2 percent of the final LoTSS survey. We
made use of \textsc{PINK}, a code which extends the SOM algorithm with rotation
and flipping invariance, increasing its suitability and effectiveness for
training on astronomical sources. After training, the SOMs can be used for a
wide range of science exploitation and we present an illustration of their
potential by finding an arbitrary number of morphologically rare sources in our
training data (424 square degrees) and subsequently in an area of the sky
($\sim$5300 square degrees) outside the training data. Objects found in this
way span a wide range of morphological and physical categories: extended jets
of radio active galactic nuclei, diffuse cluster haloes and relics, and nearby
spiral galaxies. Finally, to enable accessible, interactive, and intuitive data
exploration, we showcase the LOFAR-PyBDSF Visualisation Tool, which allows
users to explore the LoTSS dataset through the trained SOMs.
