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

• Corporate Author: SpringerLink (Online service)
• Other Authors: Nasraoui, Olfa (Editor, http://id.loc.gov/vocabulary/relators/edt), Ben N'Cir, Chiheb-Eddine (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: Unsupervised and Semi-Supervised Learning,
• Subjects: Electrical engineering.; Computational intelligence.; Data mining.; Big data.; Pattern recognition.; Communications Engineering, Networks.; Computational Intelligence.; Data Mining and Knowledge Discovery.; Big Data/Analytics.; Pattern Recognition.

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 dete...

Table of Contents:

• Introduction
• Clustering large scale data
• Clustering heterogeneous data
• Distributed clustering methods
• Clustering structured and unstructured data
• Clustering and unsupervised learning for deep learning
• Deep learning methods for clustering
• Clustering high speed cloud, grid, and streaming data
• Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis
• Large documents and textual data clustering
• Applications of big data clustering methods
• Clustering multimedia and multi-structured data
• Large-scale recommendation systems and social media systems
• Clustering multimedia and multi-structured data
• Real life applications of big data clustering
• Validation measures for big data clustering methods
• Conclusion.