  The method of Hol\'y, Sokol and \v{C}ern\'y (Applied Soft Computing, 2017,
Vol. 60, p. 752-762) clusters objects based on their incidence in a large
number of given sets. The idea is to minimize the occurrence of multiple
objects from the same cluster in the same set. In the current paper, we study
computational aspects of the method. First, we prove that the problem of
finding the optimal clustering is NP-hard. Second, to numerically find a
suitable clustering, we propose to use the genetic algorithm augmented by a
renumbering procedure, a fast task-specific local search heuristic and an
initial solution based on a simplified model. Third, in a simulation study, we
demonstrate that our improvements of the standard genetic algorithm
significantly enhance its computational performance.
