A very large scale neighborhood search algorithm for the q-mode problem

The q-mode problem is a combinatorial optimization problem that arises in the context of partitioning a given collection of data vectors with categorical attributes. A neighborhood search algorithm is proposed for solving the q-mode problem. This algorithm is based on a very large scale neighborhood that is implicitly searched using network flow techniques. The algorithm is evaluated through a computational experiment using randomly generated instances. The results show that in general this algorithm obtains very-good-quality local optima, and that in instances with strong natural clusters the algorithm consistently finds optimal or near-optimal solutions.