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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...
Müşterek Yazar: | |
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Diğer Yazarlar: | , |
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,
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Konular: | |
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.