Dissimilarity Linkage Analysis (DLA) is an extremely simple procedure for developing a typology from empirical attributes that permits the clustering of entities. First the procedure develops a taxonomy of types from empirical attributes possessed by entities in the sample. Second, the procedure assigns entities to one, and only one, type in the taxonomy. This two-step procedure clearly contrasts with many existing clustering techniques that are concerned only with the second step of this two-stage procedure. To develop a taxonomy of attribute types, the method searches for attributes that go together. A statistical test of association is first used to identify all pairs of attributes whose empirical values are significantly associated. Attribute pairs are then linked together to form serpentine clusters, each of which represents an attribute type. The attributes defining each type are not similar. In fact, the method specifically avoids using any criterion of similarity when developing the types. Each entity is then assigned to the type it most closely resembles. An entity may unequivocally fit a type. Or, if an entry does not possess all of the characteristics of a type, it is assigned to the type with which its attribute values best match. Discrete clusters of entities, based on their attribute types, are thus formed. In short, this method moves from types defined by dissimilar attributes, to clusters of similar entities in each type of the taxonomy. (Author)