  This paper demonstrates a novel and efficient unsupervised clustering method
with the combination of a Self-Organising Map (SOM) and a convolutional
autoencoder. The rapidly increasing volume of radio-astronomical data has
increased demand for machine learning methods as solutions to classification
and outlier detection. Major astronomical discoveries are unplanned and found
in the unexpected, making unsupervised machine learning highly desirable by
operating without assumptions and labelled training data. Our approach shows
SOM training time is drastically reduced and high-level features can be
clustered by training on auto-encoded feature vectors instead of raw images.
Our results demonstrate this method is capable of accurately separating
outliers on a SOM with neighbourhood similarity and K-means clustering of
radio-astronomical features complexity. We present this method as a powerful
new approach to data exploration by providing a detailed understanding of the
morphology and relationships of Radio Galaxy Zoo (RGZ) dataset image features
which can be applied to new radio survey data.
