000			03129nam a22003615i 4500
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001			88200
003			ES-MaUEC
005			20230207040627.0
007			cr nn 008mamaa
008			161107s2016 gw | s |||| 0|eng d
020			_a9783319467627
024	7		_a10.1007/978-3-319-46762-7 _2doi
040			_aES-MaUEC
050		4	_aQA276.45.R3 _bJ364 2016 EB
100	1		_aJames, Simon. _0http://id.loc.gov/authorities/names/nb2002017232 _0http://viaf.org/viaf/54198852 _9101733 _0Local
245	1	3	_aAn Introduction to Data Analysis using Aggregation Functions in R _cby Simon James.
260			_aCham, Switzerland _bSpringer _c2016
300			_a1 recurso en línea (X, 199 p.) _b29 ilustraciones, 20 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
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	7	_aOrdenadores _2embne _9138111
650	0	7	_aMatemáticas aplicadas _2embne _9145503
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-46762-7zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319467627
907			_a.b12982040 _b10-10-17 _c08-03-17
942			_2lcc _cLE
945			_aQA276.45 .R3 J364 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11603884 _z06-04-17
988			_aEBOOK, EBSPRINGER
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h3