TY  - GEN
N2  - 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.
DO  - 10.1007/978-981-33-4191-3
DO  - doi
AB  - 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.
T1  - Evolutionary data clustering :algorithms and applications /
AU  - Aljarah, Ibrahim,
AU  - Faris, Hossam,
AU  - Mirjalili, Seyedali,
CN  - Q342
ID  - 1434631
KW  - Computational intelligence.
KW  - Evolutionary computation.
KW  - Cluster analysis.
KW  - Algorithms.
KW  - Data mining.
KW  - Mathematical optimization.
KW  - Intelligence informatique.
KW  - Réseaux neuronaux à structure évolutive.
KW  - Classification automatique (Statistique)
KW  - Algorithmes.
KW  - Exploration de données (Informatique)
KW  - Optimisation mathématique.
SN  - 9789813341913
SN  - 9813341912
TI  - Evolutionary data clustering :algorithms and applications /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-4191-3
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-4191-3
ER  -