A new book has been published in Springer by Ibrahim Aljarah, Hossam Faris, and Seyedali Mirjalili and in cooperation with evo-ml members and collaborators: Raneem Qaddoura, Maria Habib, Ala’ M. Al-Zoubi, Majdi Mafarja, and Mohammad A. Hassonah, and other distinguished researchers.

The title of the book is:
Evolutionary Data Clustering: Algorithms and Applications
The book consists of 11 chapters, each chapter deals with an evolutionary method for clustering data with different applications
https://rd.springer.com/book/10.1007/978-981-33-4191-3

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.

The book includes the following chapters:
[CH1] Introduction to Evolutionary Data Clustering and Its Applications
[CH2] A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data Clustering
[CH3] A Grey Wolf-Based Clustering Algorithm for Medical Diagnosis Problems
[CH4] EEG-Based Person Identification Using Multi-Verse Optimizer as Unsupervised Clustering Techniques
[CH5] Capacitated Vehicle Routing Problem—A New Clustering Approach Based on Hybridization of Adaptive Particle Swarm Optimization and Grey Wolf Optimization
[CH6] A Hybrid Salp Swarm Algorithm with β-Hill Climbing Algorithm for Text Documents Clustering
[CH7] Controlling Population Diversity of Harris Hawks Optimization Algorithm Using Self-adaptive Clustering Approach
[CH8] A Review of Multiobjective Evolutionary Algorithms for Data Clustering Problems
[CH9] A Review of Evolutionary Data Clustering Algorithms for Image Segmentation
[CH10] Pavement Infrastructure Asset Management Using Clustering-Based Ant Colony Optimization
[CH11] A Classification Approach Based on Evolutionary Clustering and Its Application for Ransomware Detection