Generating visualizations and interpretations from high-dimensional data is a
 common problem in many fields. Two key approaches for tackling this problem 
 are clustering and representation learning. There are very performant deep
 clustering models on the one hand and interpretable representation learning techniques, 
 often relying on latent topological structures such as self-organizing maps,
 on the other hand. However, current methods do not yet successfully combine
 these two approaches. We present a new deep architecture for probabilistic clustering, 
 VarPSOM, and its extension to time series data, VarTPSOM, composed of VarPSOM 
 modules connected by LSTM cells. We show that they achieve superior 
 clustering performance compared to current deep clustering methods on static 
 MNIST/Fashion-MNIST data as well as medical time series, while inducing an
 interpretable representation. Moreover, on the medical time series, VarTPSOM
 successfully predicts future trajectories in the original data space.
We present a new deep architecture, VarPSOM, and its extension to time series data, VarTPSOM,  which achieve superior clustering performance compared to current deep clustering methods on static and temporal data.
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