Overview
- Provides a comprehensive overview of knowledge management, big data, and basic descriptive data mining methods and software
- Illustrates concepts with typical data
- Demonstrates readily available open source software
Part of the book series: Computational Risk Management (Comp. Risk Mgmt)
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Table of contents (8 chapters)
Keywords
About this book
The book seeks to provide simple explanations and demonstration of some descriptive tools. This second editionprovides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting.
Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.
Authors and Affiliations
About the authors
Desheng Wu is a Special-term Professor at University of Chinese Academy of Sciences, Beijing, China, and a Professor at Stockholm University, Sweden. He has published over 150 ISI-indexed papers in refereed journals, such as Production and Operations Management, Decision Sciences, Risk Analysis, and IEEE Transactions on Systems, Man, and Cybernetics, as well as 7 books with publishers like Springer. He is an elected member of Academia Europaea (The Academy of Europe), the European Academy of Sciences and Arts, and the International Eurasian Academy of Sciences. He has served as an associate editor and a guest editor for several journals, such as Risk Analysis, IEEE Transactions on Systems, Man, and Cybernetics, the Annals of Operations Research, Computers and Operations Research, the International Journal of Production Economics, and Omega. He is the editor of Springer’s book series on computational risk management.
Bibliographic Information
Book Title: Predictive Data Mining Models
Authors: David L. Olson, Desheng Wu
Series Title: Computational Risk Management
DOI: https://doi.org/10.1007/978-981-13-9664-9
Publisher: Springer Singapore
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2020
Hardcover ISBN: 978-981-13-9663-2Published: 21 August 2019
Softcover ISBN: 978-981-13-9666-3Published: 21 August 2020
eBook ISBN: 978-981-13-9664-9Published: 07 August 2019
Series ISSN: 2191-1436
Series E-ISSN: 2191-1444
Edition Number: 2
Number of Pages: XI, 125
Number of Illustrations: 8 b/w illustrations, 69 illustrations in colour
Topics: Big Data/Analytics, Data Mining and Knowledge Discovery, Risk Management