Practical Machine Learning with R

Chapter 6

Unsupervised Learning

Learning Objectives

By the end of this chapter, you will be able to:

• Distinguish between unsupervised and supervised learning
• Implement different techniques applied in clustering, such as soft and hard clustering, monothetic and polythetic clustering, and bottom-up versus top-down clustering
• Perform k-means clustering
• Compare performance using DIANA, AGNES, and k-means

In this chapter, we aim to equip you with a practical understanding of unsupervised learning.

Introduction

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 ...