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Clustering Methods for Big Data Analytics

Techniques, Toolboxes and Applications

  • Olfa Nasraoui
  • Chiheb-Eddine Ben N'Cir

Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Mohamed Aymen Ben HajKacem, Chiheb-Eddine Ben N’Cir, Nadia Essoussi
    Pages 1-23
  3. Sudarshan S. Chawathe
    Pages 43-72
  4. Gopi Chand Nutakki, Behnoush Abdollahi, Wenlong Sun, Olfa Nasraoui
    Pages 73-89
  5. Mariem Moslah, Mohamed Aymen Ben HajKacem, Nadia Essoussi
    Pages 91-113
  6. K. Selçuk Candan, Shengyu Huang, Xinsheng Li, Maria Luisa Sapino
    Pages 145-179
  7. Back Matter
    Pages 181-187

About this book

Introduction

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. 

Keywords

Clustering large scale data Clustering heterogeneous data Deep learning methods for clustering Applications of big data clustering methods Clustering multimedia and multi-structured data

Editors and affiliations

  • Olfa Nasraoui
    • 1
  • Chiheb-Eddine Ben N'Cir
    • 2
  1. 1.Department of Computer Engineering and Computer ScienceUniversity of LouisvilleLouisvilleUSA
  2. 2.University of JeddahJeddahSaudi Arabia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-97864-2
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-97863-5
  • Online ISBN 978-3-319-97864-2
  • Series Print ISSN 2522-848X
  • Series Online ISSN 2522-8498
  • Buy this book on publisher's site
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