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Evolutionary Data Clustering: Algorithms and Applications

 

DESCRIPTION

(Algorithms for Intelligent Systems) 1st ed. 2021 Edition 

by Ibrahim Aljarah (Editor), Hossam Faris (Editor), Seyedali Mirjalili (Editor) 

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.

DETAILS:

Year: 2021
Pages: 253
Language: English
Format: PDF
Size: 7 MB
Publisher: Springer
ISBN-10: 9813341904
ISBN-13: 978-9813341906
ASIN: B08X46Q32B
Tag: Download Book Evolutionary Data Clustering: Algorithms and Applications

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