  The task of clustering unlabeled time series and sequences entails a
particular set of challenges, namely to adequately model temporal relations and
variable sequence lengths. If these challenges are not properly handled, the
resulting clusters might be of suboptimal quality. As a key solution, we
present a joint clustering and feature learning framework for time series based
on deep learning. For a given set of time series, we train a recurrent network
to represent, or embed, each time series in a vector space such that a
divergence-based clustering loss function can discover the underlying cluster
structure in an end-to-end manner. Unlike previous approaches, our model
inherently handles multivariate time series of variable lengths and does not
require specification of a distance-measure in the input space. On a diverse
set of benchmark datasets we illustrate that our proposed Recurrent Deep
Divergence-based Clustering approach outperforms, or performs comparable to,
previous approaches.
