A general framework for mixed and incomplete data clustering based on swarm intelligence algorithms

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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.

Original languageEnglish
Article number786
JournalMathematics
Volume9
Issue number7
DOIs
StatePublished - 1 Apr 2021

Keywords

  • Artificial bee colony
  • Clustering
  • Firefly algorithm
  • Mixed and incomplete data
  • Novel bat algorithm

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