Large-scale experimental evaluation of cluster representations for multiobjective evolutionary clustering

Alvaro Garcia-Piquer, Albert Fornells, Jaume Bacardit, Albert Orriols-Puig, Elisabet Golobardes

    Producció científica: Article en revista indexadaArticleAvaluat per experts

    34 Cites (Scopus)


    Multiobjective evolutionary clustering algorithms are based on the optimization of several objective functions that guide the search following a cycle based on evolutionary algorithms. Their capabilities allow them to find better solutions than with conventional clustering algorithms if the suitable individual representation is selected. This paper provides a detailed analysis of the three most relevant and useful representations-prototype-based, label-based, and graph-based-through a wide set of synthetic data sets. Moreover, they are also compared to relevant conventional clustering algorithms. Experiments show that multiobjective evolutionary clustering is competitive with regard to other clustering algorithms. Furthermore, the best scenario for each representation is also presented.

    Idioma originalAnglès
    Número d’article6595601
    Pàgines (de-a)36-53
    Nombre de pàgines18
    RevistaIEEE Transactions on Evolutionary Computation
    Estat de la publicacióPublicada - de febr. 2014


    Navegar pels temes de recerca de 'Large-scale experimental evaluation of cluster representations for multiobjective evolutionary clustering'. Junts formen un fingerprint únic.

    Com citar-ho