Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data
Francis Ndikum Nji, Omar Faruque, Mostafa Cham, Janeja Vandana, Jianwu Wang · 2024
The increasing complexity of multidimensional spatiotemporal data presents significant challenges for clustering techniques, particularly in capturing intricate temporal, spatial, and heterogeneous patterns. This paper proposes a novel Hybrid Ensemble Deep Graph Temporal Clustering (HEDGTC) algorithm that integrates homogeneous and heterogeneous ensemble clustering models, leveraging both traditional and deep learning-based clustering approaches. The algorithm utilizes graph neural networks (GNNs) to effectively combine the strengths of multiple clustering models and enhance the clustering performance. The ensemble models are designed to handle diverse data characteristics, while the deep learning components capture complex non-linear relationships within the data. GNNs are employed to derive the final clustering outcomes by preserving spatial and temporal dependencies, making the approach well-suited for complex multidimensional spatiotemporal data. Experimental results from three real-world multivariate spatiotemporal data demonstrate the effectiveness of HEDGTC in accurately clustering and analyzing spatiotemporal patterns, outperforming state of the art ensemble models as well as traditional and individual deep clustering methods in terms of clustering performance and accuracy. The proposed method offers a robust framework for a wide range of applications, including climate modeling, geospatial analysis, and dynamic system forecasting.