Assessing partially ordered clustering in a multicriteria comparative context

•Data, which are characterized by indicators or criteria with a preference for either small or large values, are not specifically processed in pattern recognition.•Concepts from the Multicriteria Decision Aid Analysis (MCDA) field are useful to take preference characteristics into account.•Preference, dominance and Pareto frontiers are useful concepts to analyze data partitions in a multicriteria context.•For clustering problems, crossover between pattern recognition and MCDA approaches make it possible to identify relationships between clusters that are partially ordered.•Pattern recognition and MCDA offer complementary approaches to data analysis that should be shared by researchers from these two communities.