Hidden Bibliographic Details
| Other authors / contributors: | Aljarah, Ibrahim, editor.
Faris, Hossam, editor.
Mirjalili, Seyedali, editor.
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| ISBN: | 9789813341913
9813341912
9789813341906
9813341904
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| Digital file characteristics: | text file
PDF
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| Notes: | Includes bibliographical references.
Online resource; title from PDF title page (SpringerLink, viewed March 17, 2021).
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| Summary: | 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.
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| Other form: | Printed edition: 9789813341906
Printed edition: 9789813341920
Printed edition: 9789813341937
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| Standard no.: | 10.1007/978-981-33-4191-3
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