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Supervised alternative clusterings is the problem of finding a set of clusterings which are of high quality and different from a given negative clustering. The task is therefore a clear multi-objective optimization problem. Optimizing two conflicting objectives requires dealing with trade-offs. Most approaches in the literature optimize these objectives sequentially or indirectly, resulting in solutions which are dominated. The authors develop a multi-objective algorithm, called COGNAC, able to optimize the objectives directly and simultaneously and producing solutions approximating the Pareto front. COGNAC performs the recombination operator at the cluster level instead of the object level as in traditional genetic algorithms.
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