Clustering Methods for Big Data Analytics - Techniques, Toolboxes and Applications (Paperback, Softcover reprint of the original 1st ed. 2019)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Unsupervised and Semi-Supervised Learning
Release date:	2019
First published:	2019
Editors:	Olfa Nasraoui • Chiheb-Eddine Ben N'Cir
Dimensions:	235 x 155 x 11mm (L x W x T)
Format:	Paperback
Pages:	187
Edition:	Softcover reprint of the original 1st ed. 2019
ISBN-13:	978-3-03-007419-7
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Business & Economics > Business & management > Business mathematics & systems > General Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > General Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-03-007419-6
Barcode:	9783030074197

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