  The so-called black-box deep learning (DL) models are increasingly used in
classification tasks across many scientific disciplines, including wireless
communications domain. In this trend, supervised DL models appear as most
commonly proposed solutions to domain-related classification problems. Although
they are proven to have unmatched performance, the necessity for large labeled
training data and their intractable reasoning, as two major drawbacks, are
constraining their usage. The self-supervised architectures emerged as a
promising solution that reduces the size of the needed labeled data, but the
explainability problem remains. In this paper, we propose a methodology for
explaining deep clustering, self-supervised learning architectures comprised of
a representation learning part based on a Convolutional Neural Network (CNN)
and a clustering part. For the state of the art representation learning part,
our methodology employs Guided Backpropagation to interpret the regions of
interest of the input data. For the clustering part, the methodology relies on
Shallow Trees to explain the clustering result using optimized depth decision
tree. Finally, a data-specific visualizations part enables connection for each
of the clusters to the input data trough the relevant features. We explain on a
use case of wireless spectrum activity clustering how the CNN-based, deep
clustering architecture reasons.
