In all the methods we've seen so far, every sample or observation has its own target label or value. In some other cases, the dataset is unlabeled and, to extract the structure of the data, you need an unsupervised approach. In this section, we're going to introduce two methods to perform clustering, as they are among the most used methods for unsupervised learning.
It is useful to bear in mind that often the terms clustering and unsupervised learning are considered synonymous, though, actually, unsupervised learning has a larger meaning.