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Clustering Methods for Big Data Analytics = Techniques, Toolboxes and Applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Clustering Methods for Big Data Analytics/ edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir.
Reminder of title:	Techniques, Toolboxes and Applications /
other author:	Nasraoui, Olfa.
Description:	IX, 187 p. 63 illus., 31 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Electrical engineering. -
Online resource:	https://doi.org/10.1007/978-3-319-97864-2
ISBN:	9783319978642

Clustering Methods for Big Data Analytics = Techniques, Toolboxes and Applications /
Clustering Methods for Big Data AnalyticsTechniques, Toolboxes and Applications /[electronic resource] :edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir. - 1st ed. 2019. - IX, 187 p. 63 illus., 31 illus. in color.online resource. - Unsupervised and Semi-Supervised Learning,2522-848X. - Unsupervised and Semi-Supervised Learning,.

Introduction -- Clustering large scale data -- Clustering heterogeneous data -- Distributed clustering methods -- Clustering structured and unstructured data -- Clustering and unsupervised learning for deep learning -- Deep learning methods for clustering -- Clustering high speed cloud, grid, and streaming data -- Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis -- Large documents and textual data clustering -- Applications of big data clustering methods -- Clustering multimedia and multi-structured data -- Large-scale recommendation systems and social media systems -- Clustering multimedia and multi-structured data -- Real life applications of big data clustering -- Validation measures for big data clustering methods -- Conclusion.

This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation. .

ISBN: 9783319978642

Standard No.: 10.1007/978-3-319-97864-2doiSubjects--Topical Terms:

596380
Electrical engineering.

LC Class. No.: TK1-9971

Dewey Class. No.: 621.382

Clustering Methods for Big Data Analytics = Techniques, Toolboxes and Applications /
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520 $a This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation. .
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