Unsupervised Learning

Unsupervised learning aims to learn relationships within a dataset without focusing at a particular outcome. You will often hear of unsupervised learning being performed on unlabeled data. To be clear, it means it does not use the labels to guide learning - whether labels are available or not. You might, for example, perform unsupervised learning ahead of supervised learning as we shall see later. Unsupervised learning includes a number of approaches, most of which can be divided into two categories:

• Clustering: Cases are grouped together based on some derived measure of similarity / distance metric.
• Dimensionality Reduction / Matrix decomposition: Variables are combined / projected into a lower dimensional space.

In rtemis, clustering algorithms begin with c_ and decomposition/dimensionality reduction algorithms begin with d_.