國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Evolutionary data clusteringalgorith...

Aljarah, Ibrahim.

Evolutionary data clusteringalgorithms and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Evolutionary data clusteringedited by Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili.
其他題名:	algorithms and applications /
其他作者:	Aljarah, Ibrahim.
出版者:	Singapore :Springer Singapore :2021.
面頁冊數:	xii, 248 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computational intelligence.
電子資源:	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 /
LDR:02716nmm a2200337 a 4500 001 600550
003 DE-He213
005 20210521153620.0
006 m d
007 cr nn 008maaau
008 211104s2021 si s 0 eng d
020 $a 9789813341913$q(electronic bk.)
020 $a 9789813341906$q(paper)
024 7 $a 10.1007/978-981-33-4191-3 $2 doi
035 $a 978-981-33-4191-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a Q342 $b .E93 2021
245 0 0 $a Evolutionary data clustering $h [electronic resource] : $b algorithms and applications / $c edited by Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
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
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
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)