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Book 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 details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Evolutionary Data Clustering: Algorithms and Applications
• Author: Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili
• Language: English
• Binding: Paperback
• Number of pages: 248
• EAN: 9789813341937
• ID: 38637133
• Publisher: Springer Verlag, Singapore
• Weight: 403 g
• Dimensions: 235 × 155 × 15 mm
• Date of publishing: 22. February 2022

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