Linking and mining heterogeneous and multi-view data
Author(s)
Bibliographic Information
Linking and mining heterogeneous and multi-view data
（Unsupervised and semi-supervised learning / M. Emre Celebi, series editor）
Springer, c2019
- : [hardback]
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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 growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others. This book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios.
Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion;
Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others;
Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field.
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.
by "Nielsen BookData"