An evolutionary approach to multiobjective clustering

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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. © 2006 IEEE.
    Original languageEnglish
    Pages (from-to)56-76
    Number of pages20
    JournalIEEE Transactions on Evolutionary Computation
    Issue number1
    Publication statusPublished - Feb 2007


    • Clustering
    • Determination of the number of clusters
    • Evolutionary clustering
    • Model selection
    • Multiobjective clustering


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