Chapter 8. Cluster Analysis

	"Quickly bring me a beaker of wine, so that I may wet my mind and say something clever."
	--Aristophanes, Athenian Playwright

In the prior chapters, we focused on trying to learn the best algorithm in order to solve an outcome or response, for example, a breast cancer diagnosis or level of Prostate Specific Antigen. In all these cases, we had Y and that Y is a function of X or y = f(x). In our data, we had the actual Y values and we could train the Xs accordingly. This is referred to as supervised learning. However, there are many situations where we try to learn something from our data and either we do not have the Y or we actually choose to ignore it. If so, we enter the world of unsupervised learning. In this world, we build and select our algorithm based on how well it addresses our business needs versus how accurate it is.

Why would we try and learn without supervision? First of all, unsupervised learning can help you understand...