Lecture 23 – Clustering
Presented by Anthony D. Joseph
Content by Josh Hug
The Quick Check for this lecture is due Monday, November 30th at 11:59PM. A random one of the following Google Forms will give you an alphanumeric code once you submit; you should take this code and enter it into the “Lecture 23” question in the “Quick Check Codes” assignment on Gradescope to get credit for submitting this Quick Check.
Introduction to clustering. Examples of clustering in practice.
The K-Means clustering algorithm. Example of K-Means clustering.
Loss functions for K-Means. Inertia and distortion. Optimizing inertia.
Agglomerative clustering as an alternative to K-Means. Example of agglomerative clustering. Dendrograms and other clustering algorithms.
Picking the number of clusters. The elbow method and silhouette scores. Summary of clustering and machine learning.