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

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书目名称Evolutionary Data Clustering: Algorithms and Applications影响因子(影响力)




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书目名称Evolutionary Data Clustering: Algorithms and Applications网络公开度学科排名




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书目名称Evolutionary Data Clustering: Algorithms and Applications被引频次学科排名




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书目名称Evolutionary Data Clustering: Algorithms and Applications年度引用学科排名




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书目名称Evolutionary Data Clustering: Algorithms and Applications读者反馈学科排名

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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

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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.

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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.

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,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.

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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.

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