TY - BOOK ID - 7536682 TI - An Introduction to Data Analysis using Aggregation Functions in R PY - 2016 SN - 331946762X 3319467611 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Computer science. KW - Computer science KW - Computers. KW - Applied mathematics. KW - Engineering mathematics. KW - Statistics. KW - Computer Science. KW - Computing Methodologies. KW - Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. KW - Applications of Mathematics. KW - Mathematics of Computing. KW - Mathematics. KW - Statistical analysis KW - Statistical data KW - Statistical methods KW - Statistical science KW - Engineering KW - Engineering analysis KW - Automatic computers KW - Automatic data processors KW - Computer hardware KW - Computing machines (Computers) KW - Electronic brains KW - Electronic calculating-machines KW - Electronic computers KW - Hardware, Computer KW - Computer mathematics KW - Discrete mathematics KW - Electronic data processing KW - Informatics KW - Mathematics KW - Artificial intelligence. KW - Artificial Intelligence. KW - Econometrics KW - Science KW - Math KW - AI (Artificial intelligence) KW - Artificial thinking KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Mathematical statistics KW - Data processing. KW - StatisticsĀ . KW - Computer scienceāMathematics. KW - Mathematical analysis KW - R (Computer program language). KW - GNU-S (Computer program language) KW - Domain-specific programming languages UR - https://www.unicat.be/uniCat?func=search&query=sysid:7536682 AB - 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. ER -