000			05713cam a2200721 i 4500
001			ocn824686642
003			OCoLC
005			20171112124233.0
006			m o d
007			cr |||||||||||
008			130118s2013 nju ob 001 0 eng
010			_a 2013002488
020			_a9781118596289 _qelectronic bk.
020			_a1118596285 _qelectronic bk.
020			_a9781118593745 _qelectronic bk.
020			_a111859374X _qelectronic bk.
020			_a9781118572153 _qelectronic bk.
020			_a1118572157 _qelectronic bk.
020			_z9781118572221
020			_z111857222X
020			_z9781118447147 _q(cloth)
020			_z111844714X _q(cloth)
028	0	1	_aEB00063900 _bRecorded Books
029	1		_aAU@ _b000053295486
029	1		_aAU@ _b000053297900
029	1		_aCHNEW _b000720270
029	1		_aDEBBG _bBV041637074
029	1		_aDEBSZ _b431428743
029	1		_aDKDLA _b820120-katalog:000652797
029	1		_aNLGGC _b358120942
029	1		_aNZ1 _b15142930
029	1		_aNZ1 _b15340518
035			_a(OCoLC)824686642 _z(OCoLC)847348402 _z(OCoLC)849724445 _z(OCoLC)855115737 _z(OCoLC)864915196
037			_a0B305E2E-E901-4EAB-9139-89A2D33817FF _bOverDrive, Inc. _nhttp://www.overdrive.com
040			_aDLC _beng _erda _epn _cDLC _dDG1 _dN$T _dYDXCP _dCUS _dE7B _dNOC _dOCLCF _dMERUC _dEBLCP _dMHW _dIAI _dB24X7 _dUPM _dRECBK _dDEBSZ _dOCLCQ _dRRP _dTEFOD
042			_apcc
049			_aMAIN
050	0	0	_aQA76.9.D343
072		7	_aCOM _x021030 _2bisacsh
082	0	0	_a006.3/12 _223
100	1		_aLedolter, Johannes.
245	1	0	_aData mining and business analytics with R / _cJohannes Ledolter, University of Iowa. _h[electronic resource]
264		1	_aHoboken, New Jersey : _bJohn Wiley & Sons, Inc., _c[2013]
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction -- Processing the information and getting to know your data -- Standard linear regression -- Local polynomial regression: a nonparametric regression approach -- Importance of parsimony in statistical modeling -- Penalty-based variable selection in regression models with many parameters (LASSO) -- Logistic regression -- Binary classification, probabilities, and evaluating classification performance -- Classification using a nearest neighbor analysis -- The Naïve Bayesian analysis: a model predicting a categorical response from mostly categorical predictor variables -- Multinomial logistic regression -- More on classification and a discussion on discriminant analysis -- Decision trees -- Further discussion on regression and classification trees, computer software, and other useful classification methods -- Clustering -- Market basket analysis: association rules and lift -- Dimension reduction: factor models and principal components -- Reducing the dimension in regressions with multicollinear inputs: principal components regression and partial least squares -- Text as data: text mining and sentiment analysis -- Network data -- Appendices: A. Exercises -- B. References.
520			_aCollecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: * A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools * Illustrations of how to use the outlined concepts in real-world situations * Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials * Numerous exercises to help readers with computing skills and deepen their understanding of the material. Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
588	0		_aPrint version record and CIP data provided by publisher.
650		0	_aData mining.
650		0	_aR (Computer program language)
650		0	_aCommercial statistics.
650		7	_aCOMPUTERS _xDatabase Management _xData Mining. _2bisacsh
650		7	_aCommercial statistics. _2fast _0(OCoLC)fst00869640
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aR (Computer program language) _2fast _0(OCoLC)fst01086207
655		4	_aElectronic books.
776	0	8	_iPrint version: _aLedolter, Johannes. _tBusiness analytics and data mining with R. _dHoboken, New Jersey : John Wiley & Sons, Inc., [2013] _z9781118447147 _w(DLC) 2013000330
856	4	0	_uhttp://onlinelibrary.wiley.com/book/10.1002/9781118596289 _zWiley Online Library
942			_2ddc _cBK
999			_c206363 _d206363