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.

Introduction to Evolutionary Data Clustering and its Applications.- A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data Clustering.- A Grey Wolf based Clustering Algorithm for Medical Diagnosis Problems.- EEG-based Person Identification Using Multi-Verse Optimizer As Unsupervised Clustering Techniques.- Review of Evolutionary Data Clustering Algorithms for Image Segmentation.- Classification Approach based on Evolutionary Clustering and its Application for Ransomware Detection.