Linking and Mining Heterogeneous and Multi-view Data

This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of gro...

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Detaylı Bibliyografya
Müşterek Yazar: SpringerLink (Online service)
Diğer Yazarlar: P, Deepak (Editör), Jurek-Loughrey, Anna (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.
Signal processing.
Pattern recognition systems.
Artificial intelligence.
Data mining.
Communications Engineering, Networks.
Signal, Speech and Image Processing.
Automated Pattern Recognition.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Online Erişim:Full-text access
İçindekiler:
  • Chapter 1. Multi-view Data Completion
  • Chapter 2. Multi-view Clustering
  • Chapter 3. Semi-supervised and Unsupervised Approaches to Record Pairs Classification in Multi-source Data Linkage
  • Chapter 4. A Review of Unsupervised and Semi-Supervised Blocking Methods for Record Linkage
  • Chapter 5. Traffic Sensing & Assessing in Digital Transportation Systems
  • Chapter 6. How did the discussion go: Discourse act classification in social media conversations
  • Chapter 7. Entity Linking in Enterprise Search: Combining Textual and Structural Information
  • Chapter 8. Clustering Multi-view Data Using Non-negative Matrix Factorization and Manifold Learning for Effective Understanding: A Survey Paper
  • Chapter 9. Leveraging Heterogeneous Data for Fake News Detection
  • Chapter 10. On the Evaluation of Community Detection Algorithms on Heterogeneous Social Media Data
  • Chapter 11. General Framework for Multi-View Metric Learning
  • Chapter 12. Learning from imbalanced datasets with cross-view cooperation-based ensemble methods.

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