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An Introduction to Data Analysis using Aggregation Functions in R


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Produktinformationen
cover
cover
Artikel-Nr.:
     858A-9783319835792
Hersteller:
     Springer Verlag
Herst.-Nr.:
     9783319835792
EAN/GTIN:
     9783319835792
Suchbegriffe:
Allgemeine Informatikbücher
Bücher für Datenbanken - englischsp...
Datenbanken (Fachbücher)
Datenbankenbücher
This textbook helps future data analysts comprehend aggregation function theory and methods in an accessible way, focusing on a fundamental understanding of the data and summarization tools. Offering a broad overview of recent trends in aggregation research, it complements any study in statistical or machine learning techniques. Readers will learn how to program key functions in R without obtaining an extensive programming background.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.
Weitere Informationen:
Author:
Simon James
Verlag:
Springer International Publishing
Sprache:
eng
Weitere Suchbegriffe: allgemeine Informatikbücher - englischsprachig, allgemeine informatikbücher - englischsprachig, Aggregation functions, Programming in R, Data analysis, Weighted arithmetic mean, Weighted geometric mean, Weighted power means, OWA function, Choquet integral, Weight learning, Linear programming
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