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 emphasis on 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.
