Buy CLUSTERING METHODS FOR BIG DATA ANALYTICS | Technical Books, New Arrivals

ABOUT THE BOOK

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.

TABLE OF CONTENTS

1. Overview of Scalable Partitional Methods for Big Data Clustering

   • Mohamed Aymen Ben Hajkacem, Chiheb-Eddine Ben N’Cir, Nadia Essoussi

   Pages 1-23

2. Overview of Efficient Clustering Methods for High-Dimensional Big Data Streams

   • Marwan Hassani

   Pages 25-42

3. Clustering Blockchain Data

   • Sudarshan S. Chawathe

   Pages 43-72

4. An Introduction to Deep Clustering

   • Gopi Chand Nutakki, Behnoush Abdollahi, Wenlong Sun, Olfa Nasraoui

   Pages 73-89

5. Spark-Based Design of Clustering Using Particle Swarm Optimization

   • Mariem Moslah, Mohamed Aymen Ben HajKacem, Nadia Essoussi

   Pages 91-113

6. Data Stream Clustering for Real-Time Anomaly Detection: An Application to Insider Threats

   • Diana Haidar, Mohamed Medhat Gaber

   Pages 115-144

7. Effective Tensor-Based Data Clustering Through Sub-Tensor Impact Graphs

   • K. Selçuk Candan, Shengyu Huang, Xinsheng Li, Maria Luisa Sapino

   Pages 145-179

8. Back Matter

   Pages 181-187