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

Contributor(s):

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Algorithms for Intelligent SystemsPublisher: Singapore : Springer Singapore, 2021Publisher: Singapore : Imprint: Springer, 2021Edition: 1st ed. 2021Description: 1 Online-Ressource(XII, 248 p. 53 illus., 51 illus. in color.)ISBN:

9789813341913

Subject(s):

006.3 23

DOI: DOI: 10.1007/978-981-33-4191-3Online resources:

Summary: 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.Summary: 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.PPN: PPN: 1750019396Package identifier: Produktsigel: ZDB-2-INR | ZDB-2-SEB | ZDB-2-SXIT

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