  In recent years, large scale data intensive astronomical surveys have
resulted in more detailed images being produced than scientists can manually
classify. Even attempts to crowd-source this work will soon be outpaced by the
large amount of data generated by modern surveys. This has brought into
question the viability of human-based methods for classifying galaxy
morphology. While supervised learning methods require datasets with existing
labels, unsupervised learning techniques do not. Therefore, this paper
implements unsupervised learning techniques to classify the Galaxy Zoo DECaLS
dataset. A convolutional autoencoder feature extractor was trained and
implemented. The resulting features were then clustered via k-means, fuzzy
c-means and agglomerative clustering. These clusters were compared against the
true volunteer classifications provided by the Galaxy Zoo DECaLS project. The
best results, in general, were produced by the agglomerate clustering method.
However, the increase in performance compared to k-means clustering was not
significant considering the increase in clustering time. After undergoing the
appropriate clustering algorithm optimizations, this approach could prove
useful for classifying the better performing questions and could serve as the
basis for a novel approach to generating more "human-like" galaxy morphology
classifications from unsupervised techniques.
