  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.
