Evolutionary Data Clustering: Algorithms and Applications (1st ed. 2021) (Algorithms for Intelligent Systems)

By: Ibrahim Aljarah and Hossam Faris and Seyedali Mirjalili

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Synopsis: 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.

Book Quality:: Publisher Quality
ISBN-13:: 9789813341913
Related ISBNs:: 9789813341906
Publisher:: Springer Singapore, Singapore
Date of Addition:: 03/24/21
Copyrighted By:: Springer
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Computers and Internet, Technology, Mathematics and Statistics
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.
Edited by:: Ibrahim Aljarah
Edited by:: Hossam Faris
Edited by:: Seyedali Mirjalili