An Introduction To Data Analysis Using Aggregation Functions In R
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| Author | : Simon James |
| Publisher | : Springer |
| Total Pages | : 205 |
| Release | : 2016-11-07 |
| Genre | : Computers |
| ISBN | : 331946762X |
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.
| Author | : Gleb Beliakov |
| Publisher | : Springer |
| Total Pages | : 253 |
| Release | : 2019-03-19 |
| Genre | : Technology & Engineering |
| ISBN | : 3030153053 |
This book addresses computer scientists, IT specialists, mathematicians, knowledge engineers and programmers, who are engaged in research and practice of multicriteria decision making. Fuzzy measures, also known as capacities, allow one to combine degrees of preferences, support or fuzzy memberships into one representative value, taking into account interactions between the inputs. The notions of mutual reinforcement or redundancy are modeled explicitly through coefficients of fuzzy measures, and fuzzy integrals, such as the Choquet and Sugeno integrals combine the inputs. Building on previous monographs published by the authors and dealing with different aspects of aggregation, this book especially focuses on the Choquet and Sugeno integrals. It presents a number of new findings concerning computation of fuzzy measures, learning them from data and modeling interactions. The book does not require substantial mathematical background, as all the relevant notions are explained. It is intended as concise, timely and self-contained guide to the use of fuzzy measures in the field of multicriteria decision making.
| Author | : Johnson, Rick D. |
| Publisher | : IGI Global |
| Total Pages | : 742 |
| Release | : 2021-01-22 |
| Genre | : Psychology |
| ISBN | : 1799838129 |
The subject of leadership and managerial psychology exists as a sub-branch of psychology within the fields of industrial and organizational psychology. There still appears to be ongoing debate regarding the core pathology for gaining managerial expertise in professional roles relative to having suitable leadership skills and managerial knowledge beyond the direct daily work involved in organizations. Professional organizations inherently include varied levels of sensitive human interactions, which further necessitates their management professionals to have leadership styles that are adjustable contingent on a given situation. Relative to this edited book, managerial psychology is being utilized in a way that may subsequently seek to develop a series of scientific theory principles where the focus is to develop managerial axioms that advance contemporary existing knowledge surrounding professional management logic. The Handbook of Research on Multidisciplinary Perspectives on Managerial and Leadership Psychology provides value uncovered by a collaboration of generalists and specialists who bring professional managerial and leadership opinions to light through narratives and research inclusive of fundamental theory principles that can be applied in practice and academia. This edited reference is focused on the enhancement of management research through managerial psychology while highlighting topics including business process knowledge, management in diverse discipline situations and professions, corporate leadership responsibility, leadership of self and others, and leadership psychology in a variety of different fields of work. This book is ideally designed for leadership and management professionals, academicians, students, and researchers in the fields of knowledge management, administrative sciences and management, leadership development, education, and organization development sub-branches or specialty practices.
| Author | : Michel Grabisch |
| Publisher | : Cambridge University Press |
| Total Pages | : 481 |
| Release | : 2009-07-09 |
| Genre | : Computers |
| ISBN | : 0521519268 |
A rigorous and self-contained exposition of aggregation functions and their properties.
| Author | : Vitor Bianchi Lanzetta |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 414 |
| Release | : 2018-11-30 |
| Genre | : Mathematics |
| ISBN | : 1789135834 |
A hands-on guide for professionals to perform various data science tasks in R Key FeaturesExplore the popular R packages for data scienceUse R for efficient data mining, text analytics and feature engineeringBecome a thorough data science professional with the help of hands-on examples and use-cases in RBook Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learnUnderstand the R programming language and its ecosystem of packages for data scienceObtain and clean your data before processingMaster essential exploratory techniques for summarizing dataExamine various machine learning prediction, modelsExplore the H2O analytics platform in R for deep learningApply data mining techniques to available datasetsWork with interactive visualization packages in RIntegrate R with Spark and Hadoop for large-scale data analyticsWho this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course
| Author | : Hadley Wickham |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 521 |
| Release | : 2016-12-12 |
| Genre | : Computers |
| ISBN | : 1491910364 |
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
| Author | : Kurt Taylor Gaubatz |
| Publisher | : SAGE Publications |
| Total Pages | : 489 |
| Release | : 2014-04-22 |
| Genre | : Social Science |
| ISBN | : 1483346889 |
Focusing on developing practical R skills rather than teaching pure statistics, Dr. Kurt Taylor Gaubatz’s A Survivor’s Guide to R provides a gentle yet thorough introduction to R. The book is structured around critical R tasks, and focuses on applied knowledge, rather than abstract concepts. Gaubatz’s easy-to-read approach helps students with little or no background in statistics or programming to develop real-world R skills through straightforward coverage of R objects and functions. Focusing on real-world data, the challenges of dataset construction, and the use of R’s powerful graphing tools, the guide is written in an accessible, sympathetic, even humorous style that ensures students acquire functional R skills they can use in their own projects and carry into their work beyond the classroom.
| Author | : Reeta Sahoo, Gagan Sahoo |
| Publisher | : New Saraswati House India Pvt Ltd |
| Total Pages | : 500 |
| Release | : |
| Genre | : Computers |
| ISBN | : 9353621348 |
Comp-Informatic Practices-TB-12-R
| Author | : Ryan A. Estrellado |
| Publisher | : Routledge |
| Total Pages | : 315 |
| Release | : 2020-10-26 |
| Genre | : Education |
| ISBN | : 1000200906 |
Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
| Author | : Alfredo Cuzzocrea |
| Publisher | : Springer |
| Total Pages | : 472 |
| Release | : 2012-08-29 |
| Genre | : Computers |
| ISBN | : 364232584X |
This book constitutes the refereed proceedings of the 14th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2012 held in Vienna, Austria, in September 2012. The 36 revised full papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in topical sections on data warehouse design methodologies, ETL methodologies and tools, multidimensional data processing and management, data warehouse and OLAP extensions, data warehouse performance and optimization, data mining and knowledge discovery techniques, data mining and knowledge discovery applications, pattern mining, data stream mining, data warehouse confidentiality and security, and distributed paradigms and algorithms.
