Chapter Preview. The focus of this chapter is on various methods of unsupervised learning. Unsupervised learning is contrasted with supervised learning, and the role of unsupervised learning in a supervised analysis is also discussed. The concept of dimension reduction is presented first, followed by the common methods of dimension reduction, principal components/factor analysis, and clustering. More recent developments regarding classic techniques such as fuzzy clustering are then introduced. Illustrative examples that use publicly available databases are presented. At the end of the chapter there are exercises that use data supplied with the chapter. Free R code and datasets are available on the book's website.

Introduction

Even before any of us took a formal course in statistics, we were familiar with supervised learning, though it is not referred to as such. For instance, we may read in the newspaper that people who text while driving experience an increase in accidents. When the research about texting and driving was performed, there was a dependent variable (occurrence of accident or near accident) and independent variables or predictors (use of cell phone along with other variables that predict accidents).

In finance class, students may learn about the capital asset pricing model (CAPM)

R = α + βRM + ε,

where the return on an individual stock R is a constant α plus beta times the return for market RM plus an error ε.