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

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.

@article{boniol2025_2502.13049,
  title={ $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering },
  author={ Paul Boniol and Donato Tiano and Angela Bonifati and Themis Palpanas },
  journal={arXiv preprint arXiv:2502.13049},
  year={ 2025 }
}