An Evolutionary Approach to Multiobjective Clustering

An Evolutionary Approach to Multiobjective Clustering,10.1109/TEVC.2006.877146,IEEE Transactions on Evolutionary Computation,Julia Handl,Joshua D. Kno

An Evolutionary Approach to Multiobjective Clustering   (Citations: 105)
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The framework of multiobjective optimization is used to tackle the unsupervised learning problem, data clustering, following a formulation first proposed in the statistics literature. The conceptual advantages of the multiobjective formulation are discussed and an evolutionary approach to the problem is developed. The resulting algorithm, multiobjective clustering with automatic k-determination, is compared with a number of well-established single-objective clustering algorithms, a modern ensemble technique, and two methods of model selection. The experiments demonstrate that the conceptual advantages of multiobjective clustering translate into practical and scalable performance benefits
Journal: IEEE Transactions on Evolutionary Computation - TEC , vol. 11, no. 1, pp. 56-76, 2007
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