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arXiv · 2502.13049

$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering

Paul Boniol·Donato Tiano·Angela Bonifati·Themis Palpanas
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Abstract

Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, often confined to presenting users with centroids. In addressing this gap, our work presents $k$-Graph, an unsupervised method explicitly crafted to augment interpretability in time series clustering. Leveraging a graph representation of time series subsequences, $k$-Graph constructs multiple graph representations based on different subsequence lengths. This feature accommodates variable-length time series without requiring users to predetermine subsequence lengths. Our experimental results reveal that $k$-Graph outperforms current state-of-the-art time series clustering algorithms in accuracy, while providing users with meaningful explanations and interpretations of the clustering outcomes.

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Paul Boniol, Donato Tiano, Angela Bonifati, Themis Palpanas. 2025-02-18. $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering. https://arxiv.org/abs/2502.13049

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