In the last chapter, we focused on supervised learning, that is, learning from a training dataset that was labeled. In the real world, obtaining data with labels is often difficult. In many domains, it is virtually impossible to label data either due to the cost of labeling or difficulty in labeling due to the sheer volume or velocity at which data is generated. In those situations, unsupervised learning, in its various forms, offers the right approaches to explore, visualize, and perform descriptive and predictive modeling. In many applications, unsupervised learning is often coupled with supervised learning as a first step to isolate interesting data elements for labeling.
In this chapter, we will focus on various methodologies, techniques, and algorithms that are practical and well-suited for unsupervised learning. We begin by noting the issues that are common between supervised and unsupervised learning when it comes to handling data...