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Improved multi-objective clustering with automatic determination of the number of clusters

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Abstract

The multi-objective clustering with automatic determination of the number of clusters (MOCK) approach is improved in this work by means of an empirical comparison of three multi-objective evolutionary algorithms added to MOCK instead of the original algorithm used in such approach. The results of two different experiments using seven real data sets from UCI repository are reported: (1) using two multi-objective optimization performance metrics (hypervolume and two-set coverage) and (2) using the F-measure and the silhouette coefficient to evaluate the clustering quality. The results are compared against the original version of MOCK and also against other algorithms representative of the state of the art. Such results indicate that the new versions are highly competitive and able to deal with different types of data sets.

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Acknowledgments

The first author acknowledges economical support from CONACyT through scholarship No. 258800 and the academic support from the University of Veracruz to pursue graduate studies. The second author acknowledges support from CONACyT through Project No. 220522.

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Authors and Affiliations

  1. Faculty of Physics and Artificial Intelligence, Department of Artificial Intelligence, University of Veracruz, Sebastián Camacho No. 5, Xalapa, Veracruz, Mexico

    María-Guadalupe Martínez-Peñaloza, Efrén Mezura-Montes, Nicandro Cruz-Ramírez, Héctor-Gabriel Acosta-Mesa & Homero-Vladimir Ríos-Figueroa

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  1. María-Guadalupe Martínez-Peñaloza
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  2. Efrén Mezura-Montes
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  3. Nicandro Cruz-Ramírez
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  4. Héctor-Gabriel Acosta-Mesa
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  5. Homero-Vladimir Ríos-Figueroa
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Correspondence to María-Guadalupe Martínez-Peñaloza.

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Martínez-Peñaloza, MG., Mezura-Montes, E., Cruz-Ramírez, N. et al. Improved multi-objective clustering with automatic determination of the number of clusters. Neural Comput & Applic 28, 2255–2275 (2017). https://doi.org/10.1007/s00521-016-2191-1

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  • DOI: https://doi.org/10.1007/s00521-016-2191-1

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