In this chapter, we will look at the implementation of unsupervised learning. We will explore different ways of clustering; namely, bottom-up (or agglomerative) and top-down (or divisive). We will also look at the distinction between monothetic and polythetic hierarchical clustering and delve deeper into the implementation of k-means, a popular clustering technique.
Before we go into the details of the chapter, let's take a brief look at an overview of machine learning. Machine learning, in general, can be divided into three distinct groups; namely, reinforcement learning, supervised learning, and unsupervised learning, as shown in Figure 6.1. There is also one more category, semi-supervised learning, which falls between supervised learning and unsupervised learning. Most widely used learning techniques are supervised and unsupervised learning.
Figure 6.1: Types of machine learning
Reinforcement learning is a category of machine learning that focuses on training...