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21.01.2016 | Original Article

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

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

Erschienen in: Neural Computing and Applications | Ausgabe 8/2017

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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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Titel: Improved multi-objective clustering with automatic determination of the number of clusters
verfasst von: María-Guadalupe Martínez-Peñaloza
Efrén Mezura-Montes
Nicandro Cruz-Ramírez
Héctor-Gabriel Acosta-Mesa
Homero-Vladimir Ríos-Figueroa
Publikationsdatum: 21.01.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 8/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2191-1

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