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Nature Inspired Clustering – Use Cases of Krill Herd Algorithm and Flower Pollination Algorithm

  • Piotr A. KowalskiEmail author
  • Szymon Łukasik
  • Małgorzata Charytanowicz
  • Piotr Kulczycki
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Part of the Studies in Computational Intelligence book series (SCI, volume 794)

Abstract

Nature inspired metaheuristics were found to be applicable in deriving best solutions for several optimization tasks, and clustering represents a typical problem which can be successfully tackled with these methods. This paper investigates certain techniques of cluster analysis based on two recent heuristic algorithms mimicking natural processes: the Krill Herd Algorithm (KHA) and the Flower Pollination Algorithm (FPA). Beyond presenting both procedures and their implementation for clustering, a comparison with regard to quality of result was performed for fifteen data sets mainly drawn from the UCI Machine Learning Repository. As a validation of the clustering solution, the Calinski-Harabasz Index was also applied. Moreover, the performance of the investigated algorithms was assessed via Rand index value, with classic k-means procedure being employed as a point of reference. In conclusion it was established, KHA and FPA can be considered as being effective clustering tools.

Keywords

Clustering Krill herd algorithm Flower pollination algorithm Nature-Inspired algorithms Optimization Metaheuristic 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Piotr A. Kowalski
    • 1
    • 2
    Email author
  • Szymon Łukasik
    • 1
    • 2
  • Małgorzata Charytanowicz
    • 1
    • 2
    • 3
  • Piotr Kulczycki
    • 1
    • 2
  1. 1.Faculty of Physics and Applied Computer ScienceAGH University of Science and TechnologyKrakówPoland
  2. 2.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  3. 3.Institute of Mathematics and Computer ScienceThe John Paul II Catholic University of LublinLublinPoland

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