Overview
- Applies methods of modern mathematics and system analytics to the analysis of big data
- Extracts hidden regularities from these data
- Presents conventional tools of data mining and novel efficient methods and tools especially developed for this goal
Part of the book series: Studies in Big Data (SBD, volume 58)
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Table of contents (4 chapters)
Keywords
About this book
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe–Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity.
Authors and Affiliations
Bibliographic Information
Book Title: Big Data: Conceptual Analysis and Applications
Authors: Michael Z. Zgurovsky, Yuriy P. Zaychenko
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-030-14298-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-14297-1Published: 29 March 2019
eBook ISBN: 978-3-030-14298-8Published: 20 March 2019
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XXIII, 277
Number of Illustrations: 75 b/w illustrations, 54 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Big Data