Combinatorial optimization heuristics in partitioning with non Euclidean distances

We study some criteria that can be applied for the partitioning of a set of objects when non Euclidean distances are used; particularly, these criteria can be used when the data are described by binary variables. These criteria are based on aggregations that measure the homogeneity of a class and some are generalizations of variance or inertia. Properties of the criteria are studied and partitioning methods are proposed, based on metaheuristics of global optimization, such as simulated annealing and tabu search. Finally, comparative results on binary data are shown.