Multi-Metric Fusion-Based Ensemble Clustering for Time Series via Diverse Elastic Distance Functions

The growing volume of unlabeled industrial process data necessitates the development of advanced time series clustering (TSCL) algorithms for exploratory analysis. To overcome the inherent limitations of conventional single-clustering algorithms in handling complex temporal patterns and enhance robustness, a novel ensemble TSCL framework, integrating multi-metric fusion and diverse elastic distance functions, is proposed in this article. Initially, to extract multi-domain patterns, diverse base clusterers are fit for augmented time series sequences via diverse elastic distance measures, with the performance evaluated on the training subsets through multiple internal clustering validation metrics. Then, a multi-metric fusion scheme, leveraging entropy to balance the contributions of metrics, is introduced to assign adaptive weights to each clusterer, which are then incorporated into the construction of a weighted co-association matrix. To enhance the discriminative power of the similarity structure and generate robust consensus partitions, an agglomerative hierarchical clustering algorithm with average linkage is applied to the refined distance matrix, which is transformed from the co-association matrix through a nonlinear mapping. Finally, the competitiveness of the proposed algorithm is demonstrated via the comparative and ablative experiments on datasets from the UCR archive.