Evolutionary Data Clustering: Algorithms and Applications

Released on by Ibrahim Aljarah
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  • Evolutionary Data Clustering: Algorithms and Applications Book Detail

  • Author : Ibrahim Aljarah
  • Release Date : 2021-02-20
  • Publisher : Springer Nature
  • Genre : Technology & Engineering
  • Pages : 248
  • ISBN 13 : 9813341912
  • File Size : 61,61 MB
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Evolutionary Data Clustering: Algorithms and Applications by Ibrahim Aljarah PDF Summary

Book Description: This book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering in diverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.

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Genetic Algorithms for Pattern Recognition

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File Size : 31,31 MB
Total View : 6497 Views
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Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the w