Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data

TL;DR

This paper introduces HEDGTC, a hybrid ensemble deep graph temporal clustering method that enhances clustering accuracy and stability for complex multivariate spatiotemporal data by integrating multiple ensemble strategies and graph attention autoencoders.

Contribution

The paper presents a novel hybrid ensemble deep graph temporal clustering approach specifically designed for multivariate spatiotemporal data, addressing noise and misclassification issues.

Findings

HEDGTC outperforms existing ensemble clustering models on real-world datasets.

The method demonstrates improved stability and performance in capturing temporal patterns.

Results show consistent clustering accuracy across different complex datasets.

Abstract

Classifying subsets based on spatial and temporal features is crucial to the analysis of spatiotemporal data given the inherent spatial and temporal variability. Since no single clustering algorithm ensures optimal results, researchers have increasingly explored the effectiveness of ensemble approaches. Ensemble clustering has attracted much attention due to increased diversity, better generalization, and overall improved clustering performance. While ensemble clustering may yield promising results on simple datasets, it has not been fully explored on complex multivariate spatiotemporal data. For our contribution to this field, we propose a novel hybrid ensemble deep graph temporal clustering (HEDGTC) method for multivariate spatiotemporal data. HEDGTC integrates homogeneous and heterogeneous ensemble methods and adopts a dual consensus approach to address noise and misclassification from traditional clustering. It further applies a graph attention autoencoder network to improve clustering performance and stability. When evaluated on three real-world multivariate spatiotemporal data, HEDGTC outperforms state-of-the-art ensemble clustering models by showing improved performance and stability with consistent results. This indicates that HEDGTC can effectively capture implicit temporal patterns in complex spatiotemporal data.