# Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis

arXiv: https://arxiv.org/abs/2101.03742 · HTML: https://mlsanity.com/paper/2101.03742/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 11 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)

## Built on by (most cited first, 2 of 2)

- [Automated Label Generation for Time Series Classification with Representation Learning: Reduction of Label Cost for Training](https://mlsanity.com/paper/2107.05458/) (2021)
- [Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training](https://mlsanity.com/paper/2205.07870/) (2022)

## Abstract (arXiv)

Getting a robust time-series clustering with best choice of distance measure and appropriate representation is always a challenge. We propose a novel mechanism to identify the clusters combining learned compact representation of time-series, Auto Encoded Compact Sequence (AECS) and hierarchical clustering approach. Proposed algorithm aims to address the large computing time issue of hierarchical clustering as learned latent representation AECS has a length much less than the original length of time-series and at the same time want to enhance its performance.Our algorithm exploits Recurrent Neural Network (RNN) based under complete Sequence to Sequence(seq2seq) autoencoder and agglomerative hierarchical clustering with a choice of best distance measure to recommend the best clustering. Our scheme selects the best distance measure and corresponding clustering for both univariate and multivariate time-series. We have experimented with real-world time-series from UCR and UCI archive taken from diverse application domains like health, smart-city, manufacturing etc. Experimental results show that proposed method not only produce close to benchmark results but also in some cases outperform the benchmark.