Evolutionary Data Clustering: Algorithms and Applications / / edited by Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili

• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021
• Edizione: 1st ed. 2021.
• Descrizione fisica: 1 online resource (253 pages) : illustrations
• Disciplina: 518.1
• Soggetto topico: Computational intelligence; Algorithms; Data mining; Mathematical optimization; Computational Intelligence; Data Mining and Knowledge Discovery; Optimization
• Persona (resp. second.): FarisHossam; MirjaliliSeyedali; AljarahIbrahim
• Nota di contenuto: 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.
• Sommario/riassunto: 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 indiverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.
• Titolo autorizzato: Evolutionary data clustering
• ISBN: 981-334-191-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910483304803321
• Lo trovi qui: Univ. Federico II
• Serie: Algorithms for Intelligent Systems, . 2524-7573