Interpretable Time Series Clustering Using Local Explanations
This provides interpretability for time series clustering, which is often opaque, though it is incremental as it adapts existing interpretability methods to this domain.
The study tackled the problem of explaining time series clustering models by using local interpretability methods on classification models that estimate cluster labels, and the results showed this approach works effectively when the classification model is accurate.
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.