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020			_a9789813341913
024	7		_a10.1007/978-981-33-4191-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aQA76.9.A43 _b2021 EB
245	0	0	_aEvolutionary Data Clustering: Algorithms and Applications _cedited by Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili.
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XII, 248 páginas) _b53 ilustraciones, 51 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2
490	0		_aAlgorithms for Intelligent Systems _x2524-7565
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_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.
520	3		_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 in diverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.
988			_aSpringer_Robotics_2021
650		7	_2embne _aAlgoritmos _9141162
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aAljarah, Ibrahim _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFaris, Hossam _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMirjalili, Seyedali _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9789813341906
776	0	8	_iPrinted edition: _z9789813341920
776	0	8	_iPrinted edition: _z9789813341937
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-33-4191-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2021 _dz _eIG _zSI