Chapter 12. Unsupervised Machine Learning
This chapter covers the following topics:
- Clustering data with hierarchical clustering
- Cutting trees into clusters
- Clustering data with the k-means method
- Clustering data with the density-based method
- Extracting silhouette information from clustering
- Comparing clustering methods
- Recognizing digits using density-based clustering methods
- Grouping similar text documents with k-means clustering methods
- Performing dimension reduction with Principal Component Analysis (PCA)
- Determining the number of principal components using a scree plot
- Determining the number of principal components using the Kaiser method
- Visualizing multivariate data using a biplot
