Clustering Methods for Big Data Analytics - Techniques, Toolboxes and Applications (Hardcover, 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 International Publishing AG
Country of origin:	Switzerland
Series:	Unsupervised and Semi-Supervised Learning
Release date:	November 2018
First published:	2019
Editors:	Olfa Nasraoui • Chiheb-Eddine Ben N'Cir
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	187
Edition:	1st ed. 2019
ISBN-13:	978-3-319-97863-5
Categories:	Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-319-97863-2
Barcode:	9783319978635

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