Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

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.