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

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...

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Detaylı Bibliyografya
Müşterek Yazar: SpringerLink (Online service)
Diğer Yazarlar: Nasraoui, Olfa (Editör), Ben N'Cir, Chiheb-Eddine (Editör)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Cham : Springer International Publishing : 2019.
Imprint: Springer,
Edisyon:1st ed. 2019.
Seri Bilgileri:Unsupervised and Semi-Supervised Learning,
Konular:
Telecommunication.
Computational intelligence.
Data mining.
Quantitative research.
Pattern recognition systems.
Communications Engineering, Networks.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Data Analysis and Big Data.
Automated Pattern Recognition.
Online Erişim:Full-text access
İçindekiler:
  • 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.

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