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 edition provides 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 software support to data visualization. Chapter 3 covers fundamentals of market basket analysis, and Chapter 4 provides demonstration of RFM modeling, a basic marketing data mining tool. Chapter 5 demonstrates association rule mining. Chapter 6 is a more in-depth coverage of cluster analysis. Chapter 7 discusses link analysis.
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.
Reviews
“The book is direct and easy to read, explaining how methods work without in-depth scholarly references. … this is a suitable book for data mining newcomers who are not interested in a theoretical understanding of the algorithms. … the book could be used as a course resource.” (Evangelia Kavakli, Computing Reviews, November 2, 2020)
Authors and Affiliations
About the authors
Georg Lauhoff is a Technologist at Western Digital Corporation and carries out R&D in materials science and its application in data storage devices and uses the techniques described in this book for his work. He co-authored 38 refereed journal articles and over 30 conference presentations, primarily on the topic of materials science, data storage materials and magnetic thin films. He was awarded scholarships and research grants in the U.K. and Japan. He was the Clerk Maxwell Scholar from 1995– 98 and is a Fellow of the Cambridge Philosophical Society. He studied physics at Aachen (Diplom) and Cambridge University (Master and Ph.D.) specializing in the field of materials science and magnetic thin films and sensors. After graduating he moved to Japan and held a faculty position in Materials Science and Engineering at the Toyota Technological Institute and, then carried out research in the sequencing of DNA using magnetic sensors at Cambridge University before moving in 2005 to the recording industry in the Bay area.
Bibliographic Information
Book Title: Descriptive Data Mining
Authors: David L. Olson, Georg Lauhoff
Series Title: Computational Risk Management
DOI: https://doi.org/10.1007/978-981-13-7181-3
Publisher: Springer Singapore
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2019
Hardcover ISBN: 978-981-13-7180-6Published: 16 May 2019
eBook ISBN: 978-981-13-7181-3Published: 06 May 2019
Series ISSN: 2191-1436
Series E-ISSN: 2191-1444
Edition Number: 2
Number of Pages: XI, 130
Number of Illustrations: 11 b/w illustrations, 78 illustrations in colour
Topics: Big Data/Analytics, Data Mining and Knowledge Discovery, Risk Management