Evolutionary Data Clustering: Algorithms and Applications (Algorithms for Intelligent Systems),Used

Evolutionary Data Clustering: Algorithms and Applications (Algorithms for Intelligent Systems),Used

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This book provides an indepth 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 multiobjective 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 natureinspired techniques. It features a range of proven and recent natureinspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multiverse optimizer, Harris hawks optimization, betahill climbing optimization. The book also covers applications of evolutionary data clustering indiverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.

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