Evolutionary Data Clustering: Algorithms and Applications

Ibrahim Aljarah (Curatore)

Hossam Faris (Curatore)

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

• Curatore: Ibrahim Aljarah, Hossam Faris
• Editore: Springer Verlag, Singapore
• Collana: Algorithms for Intelligent Systems
• Anno: 2021
• Rilegatura: Hardback
• Pagine: 248 p.
• Dimensioni: 235 x 155 mm
• EAN: 9789813341906

Testo in English