  In this paper we develop a new unsupervised machine learning technique
comprised of a feature extractor, a convolutional autoencoder (CAE), and a
clustering algorithm consisting of a Bayesian Gaussian mixture model (BGM). We
apply this technique to visual band space-based simulated imaging data from the
Euclid Space Telescope using data from the Strong Gravitational Lenses Finding
Challenge. Our technique promisingly captures a variety of lensing features
such as Einstein rings with different radii, distorted arc structures, etc,
without using predefined labels. After the clustering process, we obtain
several classification clusters separated by different visual features which
are seen in the images. Our method successfully picks up $\sim$63\ percent of
lensing images from all lenses in the training set. With the assumed
probability proposed in this study, this technique reaches an accuracy of
$77.25\pm 0.48$\% in binary classification using the training set.
Additionally, our unsupervised clustering process can be used as the
preliminary classification for future surveys of lenses to efficiently select
targets and to speed up the labelling process. As the starting point of the
astronomical application using this technique, we not only explore the
application to gravitationally lensed systems, but also discuss the limitations
and potential future uses of this technique.
