Data Science with Python

Chapter 4

Dimensionality Reduction and Unsupervised Learning

Learning Objectives

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

• Compare hierarchical cluster analysis (HCA) and k-means clustering
• Conduct an HCA and interpret the output
• Tune a number of clusters for k-means clustering
• Select an optimal number of principal components for dimension reduction
• Perform supervised dimension compression using linear discriminant function analysis (LDA)

This chapter will cover various concepts that fall under dimensionality reduction and unsupervised learning.

Introduction

In unsupervised learning, descriptive models are used for exploratory analysis to uncover patterns in unlabeled data. Examples of unsupervised learning tasks include algorithms ...