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Titlebook: Evolutionary Data Clustering: Algorithms and Applications; Ibrahim Aljarah,Hossam Faris,Seyedali Mirjalili Book 2021 The Editor(s) (if app

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發(fā)表于 2025-3-21 18:16:55 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Evolutionary Data Clustering: Algorithms and Applications
編輯Ibrahim Aljarah,Hossam Faris,Seyedali Mirjalili
視頻videohttp://file.papertrans.cn/318/317917/317917.mp4
概述Provides an in-depth analysis of the current evolutionary clustering techniques.Features a range of proven and recent nature-inspired algorithms used to data clustering.Serves as a reference resource
叢書名稱Algorithms for Intelligent Systems
圖書封面Titlebook: Evolutionary Data Clustering: Algorithms and Applications;  Ibrahim Aljarah,Hossam Faris,Seyedali Mirjalili Book 2021 The Editor(s) (if app
描述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..
出版日期Book 2021
關(guān)鍵詞Data Clustering; Evolutionary Clustering; Nature Inspired Algorithms; Meta-heuristics; Swarm Intelligenc
版次1
doihttps://doi.org/10.1007/978-981-33-4191-3
isbn_softcover978-981-33-4193-7
isbn_ebook978-981-33-4191-3Series ISSN 2524-7565 Series E-ISSN 2524-7573
issn_series 2524-7565
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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沙發(fā)
發(fā)表于 2025-3-21 22:18:53 | 只看該作者
板凳
發(fā)表于 2025-3-22 00:39:45 | 只看該作者
2524-7565 thms used to data clustering.Serves as a reference resource 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 ev
地板
發(fā)表于 2025-3-22 07:06:55 | 只看該作者
5#
發(fā)表于 2025-3-22 10:04:29 | 只看該作者
https://doi.org/10.1007/978-3-531-93197-5timization—GWO (APGWO) for the routing phase. Our approach gives results very close to the exact solutions and better than the original k-Means algorithm. And for the routing phase, our experimental results show highly competitive solutions compared with recent approaches using PSO and GWO on many of the benchmark datasets.
6#
發(fā)表于 2025-3-22 13:12:54 | 只看該作者
Evanescent Waves in Optical Waveguides,s a review on integrating several evolutionary algorithms with clustering techniques to perform image segmentation. We choose some of the most common applications on the topic, which are Medical, Multi-objective, and Multilevel Thresholding image segmentation techniques. Then, other applications in the field are also reviewed.
7#
發(fā)表于 2025-3-22 17:54:02 | 只看該作者
8#
發(fā)表于 2025-3-22 21:17:54 | 只看該作者
,Capacitated Vehicle Routing Problem—A New Clustering Approach Based on Hybridization of Adaptive Patimization—GWO (APGWO) for the routing phase. Our approach gives results very close to the exact solutions and better than the original k-Means algorithm. And for the routing phase, our experimental results show highly competitive solutions compared with recent approaches using PSO and GWO on many of the benchmark datasets.
9#
發(fā)表于 2025-3-23 02:41:54 | 只看該作者
A Review of Evolutionary Data Clustering Algorithms for Image Segmentation,s a review on integrating several evolutionary algorithms with clustering techniques to perform image segmentation. We choose some of the most common applications on the topic, which are Medical, Multi-objective, and Multilevel Thresholding image segmentation techniques. Then, other applications in the field are also reviewed.
10#
發(fā)表于 2025-3-23 07:21:28 | 只看該作者
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