Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications (1st ed. 2019) (Unsupervised and Semi-Supervised Learning)

By: Olfa Nasraoui and Chiheb-Eddine Ben N'Cir

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Synopsis: 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.

Book Quality:: Publisher Quality
ISBN-13:: 9783319978642
Related ISBNs:: 9783319978635
Publisher:: Springer International Publishing
Date of Addition:: 12/09/18
Copyrighted By:: Springer
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Computers and Internet, Business and Finance, Technology, Communication
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.