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

arXiv: https://arxiv.org/abs/2502.13049 · HTML: https://mlsanity.com/paper/2502.13049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Fast and Accurate Time Series Classification with WEASEL](https://mlsanity.com/paper/1701.07681/) (2017)
- [Reservoir computing approaches for representation and classification of multivariate time series](https://mlsanity.com/paper/1803.07870/) (2018)
- [Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features](https://mlsanity.com/paper/1802.01059/) (2018)
- [Machine learning for Internet of Things data analysis: A survey](https://mlsanity.com/paper/1802.06305/) (2018)
- [Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series](https://mlsanity.com/paper/2008.13447/) (2020)
- [Sparsistency and agnostic inference in sparse PCA](https://mlsanity.com/paper/1401.6978/) (2014)
- [Time series clustering based on the characterisation of segment typologies](https://mlsanity.com/paper/1810.11624/) (2018)
- [Ensemble Grammar Induction For Detecting Anomalies in Time Series](https://mlsanity.com/paper/2001.11102/) (2020)
- [ParIS+: Data Series Indexing on Multi-Core Architectures](https://mlsanity.com/paper/2009.00166/) (2020)
- [SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations](https://mlsanity.com/paper/2108.11523/) (2021)

## Built on by (most cited first, 1 of 1)

- [Fuzzy Cluster-Aware Contrastive Clustering for Time Series](https://mlsanity.com/paper/2503.22211/) (2025)

## Abstract (arXiv)

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.