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.
