K-Means and Hierarchical Clustering

Free SOA Exam SRM (Statistics for Risk Modeling) lesson in Unsupervised Learning Techniques. 24 min read, ~3,665 words.

K-means partitions n observations into a pre-specified K clusters by minimizing within-cluster sum of squares (WCSS). You must choose K before running it. K-means alternates assignment and centroid updates and converges only to a local minimum, so run at least 20 random starts and keep the lowest WCSS. Agglomerative hierarchical...