This chapter explores two primary methods of cluster analysis: hierarchical and k-means clustering. Hierarchical clustering involves the creation of a nested sequence of clusters by either merging or splitting them, whereas k-means clustering requires the number of clusters to be predetermined and optimizes the assignment of individual observations to a cluster. The chapter discusses key concepts such as proximity measures and methods for determining the optimal number of clusters. Practical guidance is given on how to perform cluster analysis using R, SPSS, and Stata. Strategies for evaluating the quality of the cluster solution are also provided.