  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.
