An effective and robust deep clustering approach for time series with spatial information

Abstract

Deep clustering of time series with spatial information is a challenging task, as the representation learning in the clustering process needs to embed both temporal and spatial features of time series data. The existing deep clustering models fail to achieve a high quality of clustering due to the lack of support for complex time patterns and spatial entities in the representation learning process. Meanwhile, these deep clustering models ignore the effects of noise and outliers. Therefore, in this paper, we propose an effective and robust deep clustering model for time series data with spatial information called ER-Spatial-DEC. Our clustering model can effectively embed complex temporal patterns and spatial entities through a representation learning method by combining a spatial transformer and a quantum representational network. We further enhance the robustness of our clustering model by utilizing a collaborative multi-contrastive learning method. The experimental results demonstrate that ER-Spatial-DEC achieves superior clustering performance compared with all baseline methods across different sequence lengths. In addition, ER-Spatial-DEC exhibits strong robustness under various levels of noise and outlier perturbations, maintaining stable clustering quality compared with baselines.