Clustering high-dimensional spatiotemporal data using an unsupervised approach is a challenging problem for many data-driven applications. Existing state-of-the-art methods for unsupervised clustering use different similarity and distance functions but focus on either spatial or temporal features of the data. Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel algorithm for the temporal clustering of high-dimensional spatiotemporal data using an unsupervised deep learning method. Inspired by the U-net architecture, DSC utilizes an autoencoder integrating CNN-RNN layers to learn latent representations of the spatiotemporal data. DSC also includes a unique layer for cluster assignment on latent representations that uses the Student's t-distribution. By optimizing the clustering loss and data reconstruction loss simultaneously, the algorithm gradually improves clustering assignments and the nonlinear mapping between low-dimensional latent feature space and high-dimensional original data space. A multivariate spatiotemporal climate dataset is used to evaluate the efficacy of the proposed method. Our extensive experiments show our approach outperforms both conventional and deep learning-based unsupervised clustering algorithms. Additionally, we compared the proposed model with its various variants (CNN encoder, CNN autoencoder, CNN-RNN encoder, CNN-RNN autoencoder, etc.) to get insight into using both the CNN and RNN layers in the autoencoder, and our proposed technique outperforms these variants in terms of clustering results.

翻译：在高维时空数据上使用无监督方法进行聚类是许多数据驱动应用中的挑战性问题。现有最先进的无监督聚类方法采用不同的相似性和距离函数，但仅关注数据的空间或时间特征。本文提出了一种名为深度时空聚类（DSC）的新型算法，该算法通过联合深度表示学习空间和时间特征，使用无监督深度学习方法实现高维时空数据的时间聚类。受U-net架构启发，DSC利用集成CNN-RNN层的自编码器学习时空数据的潜在表示。DSC还包含一个独特的层，用于基于学生t分布在潜在表示上进行聚类分配。通过同时优化聚类损失和数据重建损失，算法逐步改善聚类分配以及低维潜在特征空间与高维原始数据空间之间的非线性映射。采用多变量时空气候数据集评估所提方法的有效性。大量实验表明，我们的方法在性能上超越了传统和基于深度学习的无监督聚类算法。此外，我们将所提模型与其多种变体（如CNN编码器、CNN自编码器、CNN-RNN编码器、CNN-RNN自编码器等）进行比较，以深入了解自编码器中CNN和RNN层的联合使用效果，且我们提出的技术在聚类结果上优于这些变体。