Recent Advancements in Multi-View Data Analytics

This book provides timely studies on multi-view facets of data analytics by covering recent trends in processing and reasoning about data originating from an array of local sources. A multi-view nature of data analytics is encountered when working with a variety of real-world scenarios including clustering, consensus building in decision processes, computer vision, knowledge representation, big data, data streaming, among others.
The chapters demonstrate recent pursuits in the methodology, theory, advanced algorithms, and applications of multi-view data analytics and bring new perspectives of data interpretation. The timely book will appeal to a broad readership including both researchers and practitioners interested in gaining exposure to the rapidly growing trend of multi-view data analytics and intelligent systems.

List of contents

The Psychology of Conflictive Uncertainty.- How Multi-View Techniques Can Help in Processing Uncertainty.- Multi-View Clustering and Multi-View Models.- Rethinking Collaborative Clustering: A Practical and Theoretical Study within the Realm of Multi-View Clustering.- An Optimal Transport Framework for Collaborative Multi-View Clustering.- Data Anonymization through Multi-Modular Clustering.

Product details

• Assisted by: Chen (Editor), Shyi-Ming Chen (Editor), Witold Pedrycz (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 04.06.2023
• EAN: 9783030952419
• ISBN: 978-3-0-3095241-9
• No. of pages: 342
• Dimensions: 155 mm x 19 mm x 235 mm
• Illustrations: VIII, 342 p. 74 illus., 47 illus. in color.
• Series: Studies in Big Data
• Subject: Natural sciences, medicine, IT, technology > Technology > General, dictionaries