  Generating interpretable visualizations from complex data is a common problem
in many applications. Two key ingredients for tackling this issue are
clustering and representation learning. However, current methods do not yet
successfully combine the strengths of these two approaches. Existing
representation learning models which rely on latent topological structure such
as self-organising maps, exhibit markedly lower clustering performance compared
to recent deep clustering methods. To close this performance gap, we (a)
present a novel way to fit self-organizing maps with probabilistic cluster
assignments (PSOM), (b) propose a new deep architecture for probabilistic
clustering (DPSOM) using a VAE, and (c) extend our architecture for time-series
clustering (T-DPSOM), which also allows forecasting in the latent space using
LSTMs. We show that DPSOM achieves superior clustering performance compared to
current deep clustering methods on MNIST/Fashion-MNIST, while maintaining the
favourable visualization properties of SOMs. On medical time series, we show
that T-DPSOM outperforms baseline methods in time series clustering and time
series forecasting, while providing interpretable visualizations of patient
state trajectories and uncertainty estimation.
