Unsupervised Machine Learning for Clustering in Political and Social Research

In the age of data-driven problem-solving applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet application of methods assumes an understanding of the data structure and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering which is a prominent class of unsupervised machine learning for exploring and understanding latent non-random structure in data. A suite of widely used clustering techniques is covered in this Element in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering the following algorithms are detailed: agglomerative hierarchical clustering k-means clustering Gaussian mixture models and at a higher-level fuzzy C-means clustering DBSCAN and partitioning around medoids (k-medoids) clustering.