Evolutionary Data Clustering: Algorithms and Applications

248 Seiten, Hardcover

Kurzbeschreibung des Verlags

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 indiverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.

• Reihe: Algorithms for Intelligent Systems
• Themen: Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI)
• ISBN: 9789813341906
• Sprache: Englisch
• Erscheinungsdatum: 21.02.2021
• Größe: 23.5 x 15.5 cm
• Verlag: Springer Singapore
• Herausgegeben von: Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili