Linking and Mining Heterogeneous and Multi-view Data

• Corporate Author: SpringerLink (Online service)
• Other Authors: P, Deepak (Editor, http://id.loc.gov/vocabulary/relators/edt), Jurek-Loughrey, Anna (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.; Signal processing.; Image processing.; Speech processing systems.; Pattern recognition.; Artificial intelligence.; Data mining.; Communications Engineering, Networks.; Signal, Image and Speech Processing.; Pattern Recognition.; Artificial Intelligence.; Data Mining and Knowledge Discovery.

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...

Table of Contents:

• 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.