Evolutionary Data Clustering: Algorithms and Applications
Seyedali Mirjalili editor Hossam Faris editor Ibrahim Aljarah editor
Format:Paperback
Publisher:Springer Verlag, Singapore
Published:22nd Feb '22
Currently unavailable, and unfortunately no date known when it will be back
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.
ISBN: 9789813341937
Dimensions: unknown
Weight: 403g
248 pages
1st ed. 2021