# Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series

arXiv: https://arxiv.org/abs/1811.12050 · HTML: https://mlsanity.com/paper/1811.12050/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks](https://mlsanity.com/paper/2112.07239/) (2021)
- [Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications](https://mlsanity.com/paper/2405.04061/) (2024)

## Abstract (arXiv)

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.