Overview
- Provides an overview of aggregation functions
- Includes hands-on tutorials on how to program the functions covered in R without needing extensive programming courses
- Does not assume a mathematics background
- Includes supplementary material: sn.pub/extras
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Table of contents(6 chapters)
About this book
Sections of the textbook cover background information and context, aggregating data with averaging functions, power means, and weighted averages including the Borda count. It explains how to transform data using normalization or scaling and standardization, as well as log, polynomial, and rank transforms. The section on averaging with interaction introduces OWS functions and the Choquet integral, simple functions that allow the handling of non-independent inputs. The final chapters examine software analysis with an emphasison parameter identification rather than technical aspects.
This textbook is designed for students studying computer science or business who are interested in tools for summarizing and interpreting data, without requiring a strong mathematical background. It is also suitable for those working on sophisticated data science techniques who seek a better conception of fundamental data aggregation. Solutions to the practice questions are included in the textbook.
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Authors and Affiliations
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School of Information Technology, Deakin University, Burwood, Australia
Simon James
About the author
Bibliographic Information
Book Title: An Introduction to Data Analysis using Aggregation Functions in R
Authors: Simon James
DOI: https://doi.org/10.1007/978-3-319-46762-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2016
Hardcover ISBN: 978-3-319-46761-0Published: 17 November 2016
Softcover ISBN: 978-3-319-83579-2Published: 29 June 2018
eBook ISBN: 978-3-319-46762-7Published: 07 November 2016
Edition Number: 1
Number of Pages: X, 199
Number of Illustrations: 9 b/w illustrations, 20 illustrations in colour
Topics: Artificial Intelligence, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Applications of Mathematics, Mathematics of Computing