Multiobjective evolutionary clustering algorithms are based on the optimization of several objective functions that guide the search following a cycle based on evolutionary algorithms. Their capabilities allow them to find better solutions than with conventional clustering algorithms if the suitable individual representation is selected. This paper provides a detailed analysis of the three most relevant and useful representations-prototype-based, label-based, and graph-based-through a wide set of synthetic data sets. Moreover, they are also compared to relevant conventional clustering algorithms. Experiments show that multiobjective evolutionary clustering is competitive with regard to other clustering algorithms. Furthermore, the best scenario for each representation is also presented.