Deep clustering model for time-series data based on recurrence plot and variational auto-encoder

Cheng Xu; Junjie Chen

doi:10.1117/12.2685689

28 July 2023 Deep clustering model for time-series data based on recurrence plot and variational auto-encoder

Cheng Xu, Junjie Chen

Proceedings Volume 12716, Third International Conference on Digital Signal and Computer Communications (DSCC 2023); 127160G (2023) https://doi.org/10.1117/12.2685689
Event: Third International Conference on Digital Signal and Computer Communications (DSCC 2023), 2023, Xi'an, China

Abstract

Time-series data are trendy and periodic, and have high data dimensionality, the current clustering methods cannot effectively target these characteristics. A recurrence plot variational auto-encoder deep clustering (RPVAEDC) model based on recurrence plot and variational auto-encoder is proposed to address the characteristics of time-series data. The time-series data are first transformed into recurrence plots to reveal their trends and periodicity; then the recurrence plots are fed into a deep clustering model for feature extraction and dimensionality reduction, and the distribution of the transformed data is normalized by variational auto-encoder; then the clustering results are obtained by adding a clustering layer to combine the auto-encoder reconstruction loss and clustering loss. It is experimentally demonstrated that the silhouette coefficient scores are achieved significantly better than other clustering algorithms on the public data sets.

Citation Download Citation

Cheng Xu and Junjie Chen "Deep clustering model for time-series data based on recurrence plot and variational auto-encoder", Proc. SPIE 12716, Third International Conference on Digital Signal and Computer Communications (DSCC 2023), 127160G (28 July 2023); https://doi.org/10.1117/12.2685689

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