Evolutionary Data Clustering: Algorithms and Applications (Hardcover, 1st ed. 2021)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Algorithms for Intelligent Systems
Release date:	February 2021
First published:	2021
Editors:	Ibrahim Aljarah • Hossam Faris • Seyed Ali Mirjalili
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	248
Edition:	1st ed. 2021
ISBN-13:	978-981-3341-90-6
Categories:	Books > Science & Mathematics > Mathematics > Numerical analysis Books > Science & Mathematics > Mathematics > Optimization > General Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	981-3341-90-4
Barcode:	9789813341906

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