This study focuses on exploring the use of local interpretability methods for explaining time series clustering models. Many of the state-of-the-art clustering models are not directly explainable. To provide explanations for these clustering algorithms, we train classification models to estimate the cluster labels. Then, we use interpretability methods to explain the decisions of the classification models. The explanations are used to obtain insights into the clustering models. We perform a detailed numerical study to test the proposed approach on multiple datasets, clustering models, and classification models. The analysis of the results shows that the proposed approach can be used to explain time series clustering models, specifically when the underlying classification model is accurate. Lastly, we provide a detailed analysis of the results, discussing how our approach can be used in a real-life scenario.

翻译：本研究的重点是探索使用当地可解释的方法解释时间序列群集模型。许多最先进的群集模型不能直接解释。为了解释这些群集算法,我们训练分类模型来估计群集标签。然后,我们用可解释的方法解释分类模型的决定。我们用这些解释来了解群集模型。我们进行详细的数字研究,以测试关于多个数据集、群集模型和分类模型的拟议方法。对结果的分析表明,提议的方法可以用来解释时间序列群集模型,特别是当基本分类模型准确时。最后,我们详细分析结果,讨论如何在现实生活中使用我们的方法。