05713cam a2200721 i 4500 ocn824686642 OCoLC 20171112124233.0 m o d cr ||||||||||| 130118s2013 nju ob 001 0 eng 2013002488 9781118596289 electronic bk. 1118596285 electronic bk. 9781118593745 electronic bk. 111859374X electronic bk. 9781118572153 electronic bk. 1118572157 electronic bk. 9781118572221 111857222X 9781118447147 (cloth) 111844714X (cloth) EB00063900 Recorded Books AU@ 000053295486 AU@ 000053297900 CHNEW 000720270 DEBBG BV041637074 DEBSZ 431428743 DKDLA 820120-katalog:000652797 NLGGC 358120942 NZ1 15142930 NZ1 15340518 (OCoLC)824686642 (OCoLC)847348402 (OCoLC)849724445 (OCoLC)855115737 (OCoLC)864915196 0B305E2E-E901-4EAB-9139-89A2D33817FF OverDrive, Inc. http://www.overdrive.com DLC eng rda pn DLC DG1 N$T YDXCP CUS E7B NOC OCLCF MERUC EBLCP MHW IAI B24X7 UPM RECBK DEBSZ OCLCQ RRP TEFOD pcc MAIN QA76.9.D343 COM 021030 bisacsh 006.3/12 23 Ledolter, Johannes. Data mining and business analytics with R / Johannes Ledolter, University of Iowa. [electronic resource] Hoboken, New Jersey : John Wiley & Sons, Inc., [2013] 1 online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Introduction -- 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. Collecting, 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. Print version record and CIP data provided by publisher. Data mining. R (Computer program language) Commercial statistics. COMPUTERS Database Management Data Mining. bisacsh Commercial statistics. fast (OCoLC)fst00869640 Data mining. fast (OCoLC)fst00887946 R (Computer program language) fast (OCoLC)fst01086207 Electronic books. Print version: Ledolter, Johannes. Business analytics and data mining with R. Hoboken, New Jersey : John Wiley & Sons, Inc., [2013] 9781118447147 (DLC) 2013000330 http://onlinelibrary.wiley.com/book/10.1002/9781118596289 Wiley Online Library ddc BK 206363 206363