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A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Author

Listed:
  • Yenny Villuendas-Rey

    (CIDETEC, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, GAM, CDMX 07700, Mexico)

  • Eley Barroso-Cubas

    (Facultad de Ciencias Informáticas, Universidad de Ciego de Ávila, Modesto Reyes 65100, Cuba)

  • Oscar Camacho-Nieto

    (CIDETEC, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, GAM, CDMX 07700, Mexico)

  • Cornelio Yáñez-Márquez

    (CIC, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, GAM, CDMX 07738, Mexico)

Abstract

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

Suggested Citation

  • Yenny Villuendas-Rey & Eley Barroso-Cubas & Oscar Camacho-Nieto & Cornelio Yáñez-Márquez, 2021. "A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms," Mathematics, MDPI, vol. 9(7), pages 1-24, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:7:p:786-:d:530680
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    Cited by:

    1. Yong-Hyuk Kim & Fabio Caraffini, 2023. "Preface to “Swarm and Evolutionary Computation—Bridging Theory and Practice”," Mathematics, MDPI, vol. 11(5), pages 1-3, March.

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