Clustering Methods For Big Data Analytics: Techniques, Toolboxes And Applications (Unsupervised And Semi-Supervised Learning)

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.

• | Author: Olfa Nasraoui, Chiheb-Eddine Ben N'Cir
• | Publisher: Springer
• | Publication Date: Jan 19, 2019
• | Number of Pages: 196 pages
• | Language: English
• | Binding: Paperback
• | ISBN-10: 3030074196
• | ISBN-13: 9783030074197