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

2021-01-11 08:03:57
Soma Bandyopadhyay, Anish Datta, Arpan Pal (TCS Research, TATA Consultancy Services, Kolkata, India)
     

Abstract

tract: 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.

Abstract (translated)

URL

https://arxiv.org/abs/2101.03742

PDF

https://arxiv.org/pdf/2101.03742


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