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  <titleInfo>
    <title>Customer and business analytics</title>
    <subTitle>applied data mining for business decision making using R</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Putler, Daniel S.</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Krider, Robert E.</namePart>
  </name>
  <typeOfResource>text</typeOfResource>
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    <place>
      <placeTerm type="text">Boca Raton, FL</placeTerm>
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    <publisher>CRC Press</publisher>
    <dateIssued>2015 [reprinted]</dateIssued>
    <dateIssued encoding="marc">2015</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
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    <extent>xxvi, 289 pages : illustrations ; 27 cm.</extent>
  </physicalDescription>
  <tableOfContents>I Purpose and Process Database Marketing and Data Mining Database Marketing Data Mining Linking Methods to Marketing Applications A Process Model for Data Mining-CRISP-DM History and Background The Basic Structure of CRISP-DMII Predictive Modeling Tools Basic Tools for Understanding Data Measurement Scales Software ToolsReading Data into R Tutorial Creating Simple Summary Statistics Tutorial Frequency Distributions and Histograms Tutorial Contingency Tables TutorialMultiple Linear Regression Jargon Clarification Graphical and Algebraic Representation of the Single Predictor ProblemMultiple RegressionSummary Data Visualization and Linear Regression TutorialLogistic RegressionA Graphical Illustration of the Problem The Generalized Linear Model Logistic Regression Details Logistic Regression TutorialLift Charts Constructing Lift Charts Using Lift Charts Lift Chart TutorialTree Models The Tree Algorithm Trees Models TutorialNeural Network Models The Biological Inspiration for Artificial Neural Networks Artificial Neural Networks as Predictive Models Neural Network Models TutorialPutting It All Together Stepwise Variable Selection The Rapid Model Development FrameworkApplying the Rapid Development Framework TutorialIII Grouping Methods Ward's Method of Cluster Analysis and Principal Components Summarizing Data Sets Ward's Method of Cluster Analysis Principal Components Ward's Method TutorialK-Centroids Partitioning Cluster Analysis How K-Centroid Clustering Works Cluster Types and the Nature of Customer Segments Methods to Assess Cluster Structure K-Centroids Clustering TutorialBibliography Index</tableOfContents>
  <note type="statement of responsibility">Daniel S. Putler and Robert E. Krider.</note>
  <note>Includes bibliographical references (pages. 283-285) and index.</note>
  <note>CSE</note>
  <subject authority="lcsh">
    <topic>Database marketing</topic>
    <topic>Software</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Data mining</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Decision making</topic>
    <topic>Data processing</topic>
  </subject>
  <subject authority="lcsh">
    <topic>(Computer program language)</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Database management</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Computer science</topic>
  </subject>
  <classification authority="lcc">HF5415.126 .P88 2012</classification>
  <classification authority="ddc" edition="23">658.40302855133</classification>
  <identifier type="isbn">9781466503960 (alk. paper)</identifier>
  <identifier type="isbn">1466503963 (alk. paper)</identifier>
  <identifier type="lccn">2012008925</identifier>
  <location>
    <physicalLocation>Ayesha Abed Library</physicalLocation>
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