In both regression and classification, we have a clear understanding of how the data is structured or how it behaves. Our goal is to simply model that structure or behavior. In some cases, we do not know how the data is structured. In those cases, we can utilize unsupervised learning in order to discover the structure, and thus information, within the data. The simplest form of unsupervised learning is clustering. As the name implies, clustering techniques attempt to group (or cluster) data instances. Thus, instances that belong to the same cluster share many similarities in their features, while they are dissimilar to instances that belong in separate clusters. A simple example with three clusters is depicted in the following figure. Here, the dataset features are x and y, while there is no target.

The clustering algorithm discovered three distinct groups, centered around the points (0, 0), (1, 1), and (2, 2):