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SKU/Artículo: AMZ-B08X46Q32B

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

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Kindle

Hardcover

Kindle

Paperback

Detalles del producto

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Fuera de stock

Peso con empaque:
0.36 kg

Devolución:
No

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Nuevo

Producto de:
Amazon

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USA

Sobre este producto

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.

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