Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series
This addresses the challenge of clustering time series with variable lengths and temporal dependencies for applications in data analysis, though it is incremental as it builds on existing deep learning and clustering techniques.
The paper tackled the problem of clustering unlabeled variable-length time series by developing a joint clustering and feature learning framework using a recurrent network and divergence-based loss, which outperformed or matched previous methods on benchmark datasets.
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.