Evolutionary Data Clustering

Algorithms and Applications

By: Hossam Faris (Editor), Seyedali Mirjalili (Editor), Ibrahim Aljarah (Editor)

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Hardcover | 21 February 2021

At a Glance

Hardcover
262 Pages

Dimensions(cm)
23.5 x 15.5 x 1.6

Hardcover

$279.00

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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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