Aggregation Functions A Guide For Practitioners
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Aggregation Functions: A Guide for Practitioners
A broad introduction to the topic of aggregation functions is to be found in this book. It also provides a concise account of the properties and the main classes of such functions. Some state-of-the-art techniques are presented, along with many graphical illustrations and new interpolatory aggregation functions. Particular attention is paid to identification and construction of aggregation functions from application specific requirements and empirical data.
Aggregation Functions in Theory and in Practise
Author: Humberto Bustince Sola
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-06-20
This volume collects the extended abstracts of 45 contributions of participants to the Seventh International Summer School on Aggregation Operators (AGOP 2013), held at Pamplona in July, 16-20, 2013. These contributions cover a very broad range, from the purely theoretical ones to those with a more applied focus. Moreover, the summaries of the plenary talks and tutorials given at the same workshop are included. Together they provide a good overview of recent trends in research in aggregation functions which can be of interest to both researchers in Physics or Mathematics working on the theoretical basis of aggregation functions, and to engineers who require them for applications.
An Introduction to Data Analysis using Aggregation Functions in R
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.