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Evolutionary Data Clustering: Algorithms and Applications, 1st ed. 2021 Algorithms for Intelligent Systems Series

Langue : Anglais

Coordonnateurs : Aljarah Ibrahim, Faris Hossam, Mirjalili Seyedali

Couverture de l’ouvrage Evolutionary Data Clustering: Algorithms and Applications
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.

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.

Ibrahim Aljarah is an associate professor of BIG Data Mining and Computational Intelligence at the University of Jordan-Department of Information Technology, Jordan. Currently, he is the Director of the Open Educational Resources and Blended Learning Center at The University of Jordan. He obtained his PhD in computer science from the North Dakota State University, USA, in 2014. He also obtained the master degree in computer science and information systems from the Jordan University of Science and Technology – Jordan in 2006. He obtained the bachelor degree in Computer Science from Yarmouk University - Jordan, 2003. He participated in many conferences in the field of data mining, machine learning, and Big data such as CEC, GECCO, NTIT, CSIT, IEEE NABIC, CASON, and BIGDATA Congress. Furthermore, he contributed in many projects in USA such as Vehicle Class Detection System (VCDS), Pavement Analysis Via Vehicle Electronic Telemetry (PAVVET), and Farm Cloud Storage System(CSS) projects. He has published more than 60 papers in refereed inter-national conferences and journals. His research focuses on Data Mining, Data Science, Machine Learning, Opinion Mining, Sentiment Analysis, Big Data, MapReduce, Hadoop, Swarm intelligence, Evolutionary Computation, and large-scale distributed algorithms. 

Hossam Faris is a Professor in the Information Technology Department at King Abdullah II School for Information Technology at The University of Jordan, Jordan. Hossam Faris received his B.A. and M.Sc. degrees in computer science from the Yarmouk University and Al-Balqa’ Applied University in 2004 and 2008, respectively, in Jordan. He was awarded a full-time competition-based scholarship from the Italian Ministry of Education and Research to peruse his Ph.D. degrees in e-Business at the University of Salento, Italy, where he obtained his Ph.D. degree in 2011. In 2016, he worked as a postdoctoral researcher with the GeNeura team at the Information an

Provides an in-depth analysis of the current evolutionary clustering techniques

Features a range of proven and recent nature-inspired algorithms used to data clustering

Serves as a reference resource for researchers and academicians

Date de parution :

Ouvrage de 248 p.

15.5x23.5 cm

Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).

Prix indicatif 179,34 €

Ajouter au panier

Date de parution :

Ouvrage de 248 p.

15.5x23.5 cm

Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).

Prix indicatif 179,34 €

Ajouter au panier

Thème d’Evolutionary Data Clustering: Algorithms and Applications :