Applied Unsupervised Learning with R

Chapter 2

Advanced Clustering Methods

Learning Objectives

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

• Perform k-modes clustering
• Implement DBSCAN clustering
• Perform hierarchical clustering and record clusters in a dendrogram
• Perform divisive and agglomerative clustering

In this chapter, we will have a look at some advanced clustering methods and how to record clusters in a dendrogram.

Introduction

So far, we've learned about some of the most basic algorithms of unsupervised learning: k-means clustering and k-medoids clustering. These are not only important for practical use, but are also important for understanding clustering itself.

In this chapter, we're going to study some other advanced clustering algorithms. We aren't calling ...