Attention-Guided Deep Adversarial Temporal Subspace Clustering (A-DATSC) Model for multivariate spatiotemporal data

• URL: http://arxiv.org/abs/2510.18004v1
• Date: Mon, 20 Oct 2025 18:38:26 GMT
• Title: Attention-Guided Deep Adversarial Temporal Subspace Clustering (A-DATSC) Model for multivariate spatiotemporal data
• Authors: Francis Ndikum Nji, Vandana Janeja, Jianwu Wang,
• Abstract summary: Subspace clustering models are vital for applications such as snowmelt detection sea ice tracking.<n>A graph attention based self-expressive network captures local spatial relationships, global dependencies, and both short- and long-range preserves.<n> Experiments on three real-world datasets show that A-SCSC achieves substantially superior clustering performance compared to state-of-the-art deep subspace clustering models.
• Score: 0.16206783799607727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep subspace clustering models are vital for applications such as snowmelt detection, sea ice tracking, crop health monitoring, infectious disease modeling, network load prediction, and land-use planning, where multivariate spatiotemporal data exhibit complex temporal dependencies and reside on multiple nonlinear manifolds beyond the capability of traditional clustering methods. These models project data into a latent space where samples lie in linear subspaces and exploit the self-expressiveness property to uncover intrinsic relationships. Despite their success, existing methods face major limitations: they use shallow autoencoders that ignore clustering errors, emphasize global features while neglecting local structure, fail to model long-range dependencies and positional information, and are rarely applied to 4D spatiotemporal data. To address these issues, we propose A-DATSC (Attention-Guided Deep Adversarial Temporal Subspace Clustering), a model combining a deep subspace clustering generator and a quality-verifying discriminator. The generator, inspired by U-Net, preserves spatial and temporal integrity through stacked TimeDistributed ConvLSTM2D layers, reducing parameters and enhancing generalization. A graph attention transformer based self-expressive network captures local spatial relationships, global dependencies, and both short- and long-range correlations. Experiments on three real-world multivariate spatiotemporal datasets show that A-DATSC achieves substantially superior clustering performance compared to state-of-the-art deep subspace clustering models.

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