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

• URL: http://arxiv.org/abs/2502.13049v1
• Date: Tue, 18 Feb 2025 16:59:51 GMT
• Title: $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
• Authors: Paul Boniol, Donato Tiano, Angela Bonifati, Themis Palpanas,
• Abstract summary: $k$-Graph is an unsupervised method crafted to augment interpretability in time series clustering. Our experimental results reveal that $k$-Graph outperforms current state-of-the-art time series clustering algorithms in accuracy.
• Score: 21.763409747687348
• License:
• 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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