Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications

Olfa Nasraoui (Curatore)

Chiheb-Eddine Ben N'Cir (Curatore)

Springer International Publishing AG, 2018

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.

• Curatore: Olfa Nasraoui, Chiheb-Eddine Ben N'Cir
• Editore: Springer International Publishing AG
• Collana: Unsupervised and Semi-Supervised Learning
• Anno: 2018
• Rilegatura: Hardback
• Pagine: 187 p.
• Dimensioni: 235 x 155 mm
• EAN: 9783319978635

Testo in English