  Clustering methods based on deep neural networks have proven promising for
clustering real-world data because of their high representational power. In
this paper, we propose a systematic taxonomy of clustering methods that utilize
deep neural networks. We base our taxonomy on a comprehensive review of recent
work and validate the taxonomy in a case study. In this case study, we show
that the taxonomy enables researchers and practitioners to systematically
create new clustering methods by selectively recombining and replacing distinct
aspects of previous methods with the goal of overcoming their individual
limitations. The experimental evaluation confirms this and shows that the
method created for the case study achieves state-of-the-art clustering quality
and surpasses it in some cases.
