12. Clustering

A quick dive into unsupervised methods. We cover two clutering methods: partitioning (k-means and k-medoids) and hierarchical.

Figure 1: Artwork by @allison_horst.

Agenda

November 16, 2021

4. unsupervised methods

November 18, 2021

1. k-means clustering
2. k-medoid clustering

November 23, 2021

1. hierarchical clustering

Readings

• Class notes: Unsupervised Learning

• Gareth, Witten, Hastie, and Tibshirani (2021), Unsupervised Learning (Chapter 12) Introduction to Statistical Learning.

Reflection questions

• Why does the plot of within-cluster sum of squares vs. \(k\) make an elbow-shape (hint: think about \(k\) as it ranges from 1 to \(n)?\)

• How are the centers of the clusters in \(k\)-means calculated? What about in \(k\)-medoids?

• Will a different initialization of the cluster centers always produce the same cluster output?

• How do distance metrics change a hierarchical clustering?

• How can you choose \(k\) with hierarchical clustering?

• What is the difference between single, average, and complete linkage in hierarchical clustering?

• What is the difference between agglomerative and divisive hierarchical clustering?

Ethics considerations

• What type of feature engineering is required for \(k\)-means / hierarchical clustering?

• How do you (can you?) know if your clustering has uncovered any real patterns in the data?

Slides

• In class slides - clustering for 11/18/21 and 11/23/21.

• WU19 - k-means clustering

• WU20 - hierarchical clustering

Additional Resources

• Fantastic k-means applet by Naftali Harris.

• Analyzing networks of characters in ‘Love Actually’

• Network Analysis of political books – Bridging the divide: political books