Interpreting the results

Clustering is useful for data exploration. However, it is difficult to assess the quality of clustering if data is unlabelled, which restricts the usefulness of clustering for inferencing. If class labels are known, we can apply supervised learning, which is much more powerful for predicting labels of unseen data in the testing set. We will illustrate this use case in the next exercise.