Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis
This work addresses the problem of efficient and robust time-series clustering for researchers and practitioners working with large time-series datasets, offering an incremental improvement over existing methods.
This paper tackles the challenge of robust time-series clustering by proposing a novel mechanism that combines auto-encoded compact representations (AECS) with hierarchical clustering. The method aims to reduce the computational time of hierarchical clustering by using a learned latent representation that is much shorter than the original time-series, while also enhancing performance. The experimental results show that the proposed method produces results close to benchmarks and, in some cases, outperforms them.
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.