000 03349nam a22005175i 4500
001 978-3-319-46762-7
003 DE-He213
005 20200421111849.0
007 cr nn 008mamaa
008 161107s2016 gw | s |||| 0|eng d
020 _a9783319467627
_9978-3-319-46762-7
024 7 _a10.1007/978-3-319-46762-7
_2doi
050 4 _aQA75.5-76.95
072 7 _aUMA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aCOM018000
_2bisacsh
082 0 4 _a006
_223
100 1 _aJames, Simon.
_eauthor.
245 1 3 _aAn Introduction to Data Analysis using Aggregation Functions in R
_h[electronic resource] /
_cby Simon James.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aX, 199 p. 29 illus., 20 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aAggregating data with averaging functions -- Transforming data -- Weighted averaging -- Averaging with interaction -- Fitting aggregation functions to empirical data -- Solutions.
520 _aThis 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.
650 0 _aComputer science.
650 0 _aComputer science
_xMathematics.
650 0 _aComputers.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 0 _aStatistics.
650 1 4 _aComputer Science.
650 2 4 _aComputing Methodologies.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aApplications of Mathematics.
650 2 4 _aMathematics of Computing.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319467610
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-46762-7
912 _aZDB-2-SCS
942 _cEBK
999 _c56021
_d56021