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

Description

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. Front Matter ....Pages i-ix Overview of Scalable Partitional Methods for Big Data Clustering (Mohamed Aymen Ben HajKacem, Chiheb-Eddine Ben N’Cir, Nadia Essoussi)....Pages 1-23 Overview of Efficient Clustering Methods for High-Dimensional Big Data Streams (Marwan Hassani)....Pages 25-42 Clustering Blockchain Data (Sudarshan S. Chawathe)....Pages 43-72 An Introduction to Deep Clustering (Gopi Chand Nutakki, Behnoush Abdollahi, Wenlong Sun, Olfa Nasraoui)....Pages 73-89 Spark-Based Design of Clustering Using Particle Swarm Optimization (Mariem Moslah, Mohamed Aymen Ben HajKacem, Nadia Essoussi)....Pages 91-113 Data Stream Clustering for Real-Time Anomaly Detection: An Application to Insider Threats (Diana Haidar, Mohamed Medhat Gaber)....Pages 115-144 Effective Tensor-Based Data Clustering Through Sub-Tensor Impact Graphs (K. Selçuk Candan, Shengyu Huang, Xinsheng Li, Maria Luisa Sapino)....Pages 145-179 Back Matter ....Pages 181-187