Cluster analysis is designed for partitioning a set of objects into homogeneous classes by using observed data which carry information on the mutual similarity or dissimilarity of objects. Clustering methods are often defined in a heuristic or algorithmic way, emphasizing computational aspects and heuristic motivations. In contrast, this paper considers the clustering problem in a probabilistic framework and presents a survey on probabilistic models for partition-type clustering structures. It is shown how clustering criteria and grouping methods may be derived from these models in the case of vectorvalued data, dissimilarity matrices and similarity relations.