This paper surveys various ways in which probabilistic approaches can be useful in partitional ('non-hierarchical') cluster analysis. Four basic distribution models for 'clustering structures' are described in order to derive suitable clustering strategies. They are exemplified for various special distribution cases, including dissimilarity data and random similarity relations. A special section describes statistical tests for checking the relevance of a calculated classification (e.g., the max-F test, convex cluster tests) and comparing it to standard clustering situations by a strategy called comparative assessment of classifications (CAC).