Interpretable Clustering of Spatio-Temporal Data by using SHAP Method

Nowadays, the simultaneous consideration of spatial features and temporal features in the form of spatiotemporal data has been considered in various applications. Among the most important of these applications, we can mention the classification of drivers, the personality of users, the prediction and monitoring of the route and many other cases. Due to the lack of labels for this type of data, unsupervised methods are the only analytical methods on the table. Today, machine learning or deep learning algorithms are very popular and widely used due to the many successes they have had in solving various problems, but due to the black box nature of these methods, the internal mechanism of these models is unclear to users. On the other hand, in addition to the high accuracy of the model, the interpretability and root-finding of analyzes based on spatial-temporal data are very important. In this paper, we intend to cluster spatio-temporal data by focusing on the interpretability of results and selected interpretable features.