Recent Advancements in Multi-View Data Analytics

The book provides timely studies on multi-view facets of data analytics, covering recent trends in processing and reasoning about data from various local sources. It appeals to a broad readership interested in gaining exposure to the rapidly growing trend of multi-view data analytics and intelligent systems.

Format: Hardback
Length: 342 pages
Publication date: 21 May 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into the latest trends and advancements in multi-view data analytics, offering insightful studies on various aspects of processing and reasoning about data originating from diverse local sources. Multi-view data analytics arises when dealing with a wide range of real-world scenarios, such as clustering, consensus building in decision-making processes, computer vision, knowledge representation, big data, data streaming, and more.

The chapters in this book showcase recent developments in multi-view data analytics methodology, theory, advanced algorithms, and applications. They provide fresh perspectives on data interpretation, offering valuable insights for researchers and practitioners alike. With its timely coverage, the book appeals to a broad audience, including those interested in exploring the rapidly growing field of multi-view data analytics and intelligent systems.

The book's chapters are organized into three sections:

Section 1: Introduction to Multi-View Data Analytics

This section provides an overview of multi-view data analytics, its significance, and the challenges it presents. It discusses the importance of considering multiple perspectives when analyzing data and highlights the potential benefits of integrating data from different sources.

Section 2: Methodology and Theory

In this section, the book explores the methodologies and theoretical foundations of multi-view data analytics. It discusses various approaches, such as feature fusion, dimensionality reduction, and visualization, that are used to analyze and interpret multi-view data. The section also covers the development of new algorithms and models for multi-view data analysis, including machine learning techniques and statistical methods.

Section 3: Advanced Algorithms and Applications

The third section showcases advanced algorithms and applications of multi-view data analytics. It discusses real-world case studies and applications in various domains, such as healthcare, finance, marketing, and social sciences. The chapters provide practical insights and examples of how multi-view data analytics can be used to solve complex problems and gain valuable insights.

In conclusion, this comprehensive book provides a valuable resource for researchers and practitioners interested in multi-view data analytics and intelligent systems. It offers timely studies on the latest trends, methodologies, and applications, helping readers gain a deeper understanding of this rapidly growing field. By exploring the multi-view nature of data analytics, the book opens up new avenues for data interpretation and analysis, enabling organizations to make informed decisions and drive innovation.

Weight: 694g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030952389
Edition number: 1st ed. 2022