Cluster analysis is a procedure for grouping cases (objects of investigation) in a data set. For this purpose, the first step is to determine the similarity or dissimilarity (distance) between the cases by a suitable measure. The second step searches for the fusion algorithm which combines the individual cases successively into groups (clusters). The goal is to combine such cases into groups which are similar with respect to the considered segmentation variables (homogenous groups). At the same time, the groups should be as dissimilar as possible. The procedures of cluster analysis can handle variables with metric, non-metric as well as mixed scales. The focus of the chapter is on hierarchical agglomerative clustering methods, with the single-linkage method and Ward’s method presented in detail. Finally, k-means clustering and two-step cluster analysis, two partitioning cluster methods, are also explained. These methods offer particular advantages when working with large amounts of data.