Item Details

Data Mining and Business Analytics With R [electronic resource]

Johannes Ledolter
Format
EBook; Book; Online
Published
Hoboken, N.J. : Wiley, 2013.
Language
English
Related Title
Virtual Library of Virginia EBL DDA purchased title
ISBN
9781118593745 (electronic bk.), 111859374X, 9781118447147
Summary
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.
Contents
  • Introduction
  • Processing the information and getting to know your data
  • Standard linear regression
  • Local polynomial regression : a nonparametric regression
  • 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 for 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.
Description
1 online resource (xi, 351 pages) : illustrations (some color)
Notes
Includes bibliographical references and index.
Logo for Copyright Not EvaluatedCopyright Not Evaluated
Technical Details

  • LEADER 04638cam a2200469Ma 4500
    001 ocn849724445
    003 OCoLC
    005 20140922073235.6
    006 m o d
    007 cr |n|---|||||
    008 130614s2013 njua ob 001 0 eng d
    040
      
      
    a| MERUC b| eng e| pn c| MERUC d| EBLCP d| MHW d| IAI d| OCLCQ d| B24X7 d| OCLCF
    020
      
      
    a| 9781118593745 (electronic bk.)
    020
      
      
    a| 111859374X
    020
      
      
    z| 9781118447147
    035
      
      
    a| (OCoLC)849724445
    050
      
    4
    a| QA76.9.D343 .L384 2013eb
    082
    0
    4
    a| 006.312
    049
      
      
    a| MAIN
    100
    1
      
    a| Ledolter, Johannes.
    245
    1
    0
    a| Data Mining and Business Analytics with R h| [electronic resource] / c| Johannes Ledolter.
    260
      
      
    a| Hoboken, N.J. : b| Wiley, c| 2013.
    300
      
      
    a| 1 online resource (xi, 351 pages) : b| illustrations (some color)
    336
      
      
    a| text b| txt 2| rdacontent
    337
      
      
    a| computer b| c 2| rdamedia
    338
      
      
    a| online resource b| cr 2| rdacarrier
    504
      
      
    a| Includes bibliographical references and index.
    505
    2
      
    a| Introduction -- Processing the information and getting to know your data -- Standard linear regression -- Local polynomial regression : a nonparametric regression -- 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 for 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.
    520
      
      
    a| 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.
    650
      
    0
    a| Data mining.
    650
      
    0
    a| R (Computer program language)
    650
      
    0
    a| Commercial statistics.
    650
      
    7
    a| Commercial statistics. 2| fast 0| (OCoLC)fst00869640
    650
      
    7
    a| Data mining. 2| fast 0| (OCoLC)fst00887946
    650
      
    7
    a| R (Computer program language) 2| fast 0| (OCoLC)fst01086207
    655
      
    4
    a| Electronic books.
    740
    0
      
    a| Virtual Library of Virginia EBL DDA purchased title
    776
    0
    8
    i| Print version: a| Ledolter, Johannes. t| Data Mining and Business Analytics with R. d| Hoboken : Wiley, ©2013 z| 9781118447147
    856
    4
    0
    u| http://proxy.its.virginia.edu/login?url=http://viva.eblib.com/patron/FullRecord.aspx?p=1204741&userid=^u&conl=UVA&echo=1
    938
      
      
    a| EBL - Ebook Library b| EBLB n| EBL1204741
    938
      
      
    a| Books 24x7 b| B247 n| bks00052696
    994
      
      
    a| 92 b| VA@
    999
      
      
    w| WEB l| INTERNET m| UVA-LIB t| INTERNET

Availability

Google Preview

Read Online