提出k-Graph图嵌入方法，让时间序列聚类结果更易解释。
$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
- 用不同长度子序列构建图表示，自动适应变长数据。
- 在多个数据集上聚类准确率超越现有方法。
- 适合需要理解聚类依据的工业和科研场景。
时间序列聚类在众多领域有广泛应用，但现有方法可解释性差，通常仅提供聚类中心。为此，本文提出k-Graph，一种无监督的时间序列聚类方法，旨在增强结果的可解释性。该方法通过时间序列子序列的图表示，构建多种基于不同子序列长度的图结构，无需用户预先设定子序列长度即可处理变长序列。实验表明，k-Graph在多个基准数据集上均优于当前最优聚类算法，在保持高准确率的同时，为用户提供有意义的聚类解释。该方法尤其适用于需理解聚类逻辑的应用场景。
原文摘要 · Abstract (English)
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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