Unsupervised and Semi-Supervised Learning: Techniques, Toolboxes and Applications

Nasraoui, Olfa; Ben N'Cir, Chiheb-Eddine

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.

Details

Published by: Springer

Publication Date: 2018-11-08

Format: Hardcover

ISBN-13: 9783319978635

DOI: 10.1007/978-3-319-97864-2

Dimensions: 235cm x155cm

Pages: 187