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Evolutionary data clusteringalgorith...

Aljarah, Ibrahim.

Evolutionary data clusteringalgorithms and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evolutionary data clusteringedited by Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili.
Reminder of title:	algorithms and applications /
other author:	Aljarah, Ibrahim.
Published:	Singapore :Springer Singapore :2021.
Description:	xii, 248 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Computational intelligence.
Online resource:	https://doi.org/10.1007/978-981-33-4191-3
ISBN:	9789813341913$q(electronic bk.)

Evolutionary data clusteringalgorithms and applications /
Evolutionary data clusteringalgorithms and applications /[electronic resource] :edited by Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili. - Singapore :Springer Singapore :2021. - xii, 248 p. :ill., digital ;24 cm. - Algorithms for intelligent systems,2524-7565. - Algorithms for intelligent systems..

Introduction to Evolutionary Data Clustering and its Applications -- A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data Clustering -- A Grey Wolf based Clustering Algorithm for Medical Diagnosis Problems -- EEG-based Person Identification Using Multi-Verse Optimizer As Unsupervised Clustering Techniques -- Review of Evolutionary Data Clustering Algorithms for Image Segmentation -- Classification Approach based on Evolutionary Clustering and its Application for Ransomware Detection.

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.

ISBN: 9789813341913$q(electronic bk.)

Standard No.: 10.1007/978-981-33-4191-3doiSubjects--Topical Terms:

210824
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Evolutionary data clusteringalgorithms and applications /
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245 0 0 $a Evolutionary data clustering $h [electronic resource] : $b algorithms and applications / $c edited by Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili.
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300 $a xii, 248 p. : $b ill., digital ; $c 24 cm.
490 1 $a Algorithms for intelligent systems, $x 2524-7565
505 0 $a Introduction to Evolutionary Data Clustering and its Applications -- A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data Clustering -- A Grey Wolf based Clustering Algorithm for Medical Diagnosis Problems -- EEG-based Person Identification Using Multi-Verse Optimizer As Unsupervised Clustering Techniques -- Review of Evolutionary Data Clustering Algorithms for Image Segmentation -- Classification Approach based on Evolutionary Clustering and its Application for Ransomware Detection.
520 $a 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.
650 0 $a Computational intelligence. $3 210824
650 0 $a Evolutionary computation. $3 231709
650 0 $a Cluster analysis. $3 182711
650 0 $a Algorithms. $3 184661
650 0 $a Data mining. $3 184440
650 0 $a Mathematical optimization. $3 183292
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Optimization. $3 274084
700 1 $a Aljarah, Ibrahim. $3 861745
700 1 $a Faris, Hossam. $3 861744
700 1 $a Mirjalili, Seyedali. $3 830154
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830 0 $a Algorithms for intelligent systems. $3 857955
856 4 0 $u https://doi.org/10.1007/978-981-33-4191-3
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)