Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data
| dc.contributor.author | Faruque, Omar | |
| dc.contributor.author | Nji, Francis Ndikum | |
| dc.contributor.author | Cham, Mostafa | |
| dc.contributor.author | Salvi, Rohan Mandar | |
| dc.contributor.author | Zheng, Xue | |
| dc.contributor.author | Wang, Jianwu | |
| dc.date.accessioned | 2023-05-25T19:59:59Z | |
| dc.date.available | 2023-05-25T19:59:59Z | |
| dc.date.issued | 2023-04-27 | |
| dc.description.abstract | 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. | en_US |
| dc.description.uri | https://arxiv.org/abs/2304.14541 | en_US |
| dc.format.extent | 16 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.genre | preprints | en_US |
| dc.identifier | doi:10.13016/m2aexs-mzpv | |
| dc.identifier.uri | https://doi.org/10.48550/arXiv.2304.14541 | |
| dc.identifier.uri | http://hdl.handle.net/11603/28090 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC Joint Center for Earth Systems Technology (JCET) | |
| dc.rights | This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law. | en_US |
| dc.rights | Public Domain Mark 1.0 | * |
| dc.rights.uri | http://creativecommons.org/publicdomain/mark/1.0/ | * |
| dc.title | Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-9933-1170 | en_US |