LEADER 03647nam 22006615 450 001 9910483304803321 005 20251113193624.0 010 $a981-334-191-2 024 7 $a10.1007/978-981-33-4191-3 035 $a(CKB)4100000011773947 035 $a(MiAaPQ)EBC6485334 035 $a(PPN)253856914 035 $a(Au-PeEL)EBL6485334 035 $a(OCoLC)1243554687 035 $a(DE-He213)978-981-33-4191-3 035 $a(EXLCZ)994100000011773947 100 $a20210220d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEvolutionary Data Clustering: Algorithms and Applications /$fedited by Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili 205 $a1st ed. 2021. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2021. 215 $a1 online resource (253 pages) $cillustrations 225 1 $aAlgorithms for Intelligent Systems,$x2524-7573 311 08$a981-334-190-4 327 $aIntroduction 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. 330 $aThis book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering indiverse fields such as image segmentation, medical applications, and pavement infrastructure asset management. 410 0$aAlgorithms for Intelligent Systems,$x2524-7573 606 $aComputational intelligence 606 $aAlgorithms 606 $aData mining 606 $aMathematical optimization 606 $aComputational Intelligence 606 $aAlgorithms 606 $aData Mining and Knowledge Discovery 606 $aOptimization 615 0$aComputational intelligence. 615 0$aAlgorithms. 615 0$aData mining. 615 0$aMathematical optimization. 615 14$aComputational Intelligence. 615 24$aAlgorithms. 615 24$aData Mining and Knowledge Discovery. 615 24$aOptimization. 676 $a518.1 702 $aFaris$b Hossam 702 $aMirjalili$b Seyedali 702 $aAljarah$b Ibrahim 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483304803321 996 $aEvolutionary data clustering$92853198 997 $aUNINA