In many cases, a dataset can be clustered following several criteria that complement each other: group membership following one criterion provides little or no information regarding group membership following the other criterion. When these criteria are not known a priori, they have to be determined from the data. We put forward one method for simultaneously finding the complementary criteria and the clustering corresponding to each criterion.