Cluster analysis is perhaps the most familiar of all approaches to exploratory multivariate analysis, although it is not always thought of as a multivariate technique parallel to, for example, multidimensional scaling or principal components analysis. It is like such approaches, though, in seeking structure in the relationships among cases characterized by a number of variables. Cases that are strongly similar to each other, in terms of their values for a number of variables, wind up in the same groups or clusters, while those that are more different from each other wind up in different clusters. Cluster analysis mimics one of the human mind’s fundamental ways of dealing with complicated variability: categorizing, or putting things into groups. Artifact typology in archaeology is a very familiar example of such categorizing. Recognizing that no two artifacts are likely to be identical, but that some pairs are more similar than others, we put the more similar ones together subjectively into what we then define as types. Our artifact typologies are hierarchical in that they group artifacts first according to broad classes like ceramics, flaked stone, textiles, etc., and then, within these broad classes, into more specific types at perhaps several levels. Flaked stone, for example, may be divided into tools and debitage; tools, in turn, into unifaces and bifaces; unifaces, into scrapers, blades, burins, etc.; scrapers, into endscrapers and sidescrapers; and so on. This kind of hierarchical clustering can also be accomplished by statistical (as opposed to purely subjective) means. The first step in a hierarchical cluster analysis is usually the same as the first step in multidimensional scaling: measuring the similarities between each pair of cases in the dataset. The coefficients of similarity (or dissimilarity) that were discussed in Chapter 22 are just as suitable for clustering. Once the similarities (or dissimilarities or distances) have been measured, the clustering can begin.