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  • © 2019

Linking and Mining Heterogeneous and Multi-view Data

  • 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

Part of the book series: Unsupervised and Semi-Supervised Learning (UNSESUL)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-viii
  2. Multi-View Data Completion

    • Sahely Bhadra
    Pages 1-25
  3. Multi-View Clustering

    • Deepak P, Anna Jurek-Loughrey
    Pages 27-53
  4. A Review of Unsupervised and Semi-supervised Blocking Methods for Record Linkage

    • Kevin O’Hare, Anna Jurek-Loughrey, Cassio de Campos
    Pages 79-105
  5. Traffic Sensing and Assessing in Digital Transportation Systems

    • Hana Rabbouch, Foued Saâdaoui, Rafaa Mraihi
    Pages 107-135
  6. How Did the Discussion Go: Discourse Act Classification in Social Media Conversations

    • Subhabrata Dutta, Tanmoy Chakraborty, Dipankar Das
    Pages 137-160
  7. Leveraging Heterogeneous Data for Fake News Detection

    • K. Anoop, Manjary P. Gangan, Deepak P, V. L. Lajish
    Pages 229-264
  8. General Framework for Multi-View Metric Learning

    • Riikka Huusari, Hachem Kadri, Cécile Capponi
    Pages 265-294
  9. Back Matter

    Pages 335-343

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. 

Editors and Affiliations

  • Queen’s University Belfast, Northern Ireland, UK

    Deepak P, Anna Jurek-Loughrey

About the editors

Deepak P is currently a Lecturer (Assistant Professor) in Computer Science at Queen’s University Belfast. His research interests lie across various sub-fields of data analytics such as natural language processing, information retrieval, data mining, machine learning and databases. He has authored more than 50 research papers in top avenues in data analytics, and has ten granted patents from USPTO. Prior to joining Queen’s University in 2015, he was a researcher at IBM Research India for many years. He is a Senior Member of the IEEE and the ACM, and is a recipient of the Indian National Academy of Engineering Young Engineer Award.

Anna Jurek-Loughrey is currently a Lecturer (Assistant Professor) in Computer Science at Queen’s University Belfast. Her work has spanned a diverse set of topics in the area of data analytics comprising supervised and unsupervised machine learning, record linkage, sensor-based activity recognition within smart environments, social media analytics with application to health and security. Before joining Queen’s in 2015 she worked as a data scientist at Repknight Ltd for two years.


Bibliographic Information

Buy it now

Buying options

eBook USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access