The value of data is often associated with the learning performance (e.g.,

Therefore, estimating how well a given model might perform on the new data is an important step toward reliable ML applications. This is very challenging, however, as the data distribution can change in flexible ways, and we may not have any labels on the new data, which is often the case in monitoring settings.

Clustering is an important exploratory data analysis technique to group objects based on their similarity.