**3.2 Unsupervised learning**

Given a compound dataset, unsupervised learning can include tasks such as detecting subpopulation to determine the number of chemotypes to estimate chemical diversity and chemical space visualization. Putting in a broader perspective, the purpose of unsupervised learning is to understand the underlying pattern of the datasets. Another important problem stem from unsupervised learning is the ability to define appropriate metrics that can be used to quantify the similarity of data distributed over feature space. These metrics can be useful for chemometrics application including measuring the similarity between pairs of compounds.
