About this book
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.
Dimensionality Reduction Deep Learning Ensemble Learning Common Representation Learning Anomaly Detection Uncertainty Modelling Social Network Analysis Natural Language Processing Multi-modal Learning
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-030-01872-6
- Copyright Information Springer Nature Switzerland AG 2019
- Publisher Name Springer, Cham
- eBook Packages Engineering
- Print ISBN 978-3-030-01871-9
- Online ISBN 978-3-030-01872-6
- Series Print ISSN 2522-848X
- Series Online ISSN 2522-8498
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