$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912236162777088 |
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| author | Boniol, Paul Tiano, Donato Bonifati, Angela Palpanas, Themis |
| author_facet | Boniol, Paul Tiano, Donato Bonifati, Angela Palpanas, Themis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering Boniol, Paul Tiano, Donato Bonifati, Angela Palpanas, Themis Machine Learning 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. |
| title | $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.13049 |