Chapter 8. Clustering

In this chapter, we will cover the following topics:

  • Clustering data with hierarchical clustering
  • Cutting a tree into clusters
  • Clustering data with the k-means method
  • Drawing a bivariate cluster plot
  • Comparing clustering methods
  • Extracting silhouette information from clustering
  • Obtaining optimum clusters for k-means
  • Clustering data with the density-based method
  • Clustering data with the model-based method
  • Visualizing a dissimilarity matrix
  • Validating clusters externally

Introduction

Clustering is a technique used to group similar objects (close in terms of distance) together in the same group (cluster). Unlike supervised learning methods (for example, classification and regression) covered in the previous chapters, a clustering analysis ...

Get R: Recipes for Analysis, Visualization and Machine Learning now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.