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

Yenny Villuendas-Rey, Eley Barroso-Cubas, Oscar Camacho-Nieto and Cornelio Yáñez-Márquez
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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

Mathematics, 2021, vol. 9, issue 7, 1-24

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.

Keywords: clustering; mixed and incomplete data; artificial bee colony; firefly algorithm; novel bat algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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