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Statistical data mining using SAS applications / George Fernandez.

By: Material type: TextTextLanguage: English Series: Chapman & Hall / CRC data mining and knowledge discovery seriesPublication details: Boca Raton ; London ; New York : CRC Press, 2010.Edition: Second editionDescription: xxiii, 453 páginas : ilustraciones, gráficas, tablas ; 24 cmContent type:
  • Texto
Media type:
  • Sin mediación
Carrier type:
  • Volumen
ISBN:
  • 9781439810750
Subject(s): DDC classification:
  • 006.312  F37s  21
Other classification:
  • C80
Contents:
Preface -- Acknowledgments ; About the author -- 1. Data mining: a gentle introduction: 1.1. Introduction ; 1.2. Data mining: why it is successful in the IT world ; 1.3. Benefits of data mining ; 1.4. Data mining: users ; 1.5. Data mining: tools ; 1.6. Data mining: steps ; 1.7. Problems in the data mining process ; 1.8. SAS Software the leader in data mining ; 1.9. Introduction of User-friendly SAS macros for statistical ; 1.10. Summary -- 2. Preparing data for data mining: 2.1. Introduction ; 2.2. Data requirements in data mining ; 2.3. Ideal structures of data for data mining ; 2.4. Understanding the measurement scale of variables ; 2.5. Entire database or representative sample ; 2.6. Sampling for data mining ; 2.7. User-friendly SAS applications used in data preparation ; 2.8. Summary -- 3 Exploratory data analysis: 3.1. Introduction ; 3.2. Exploring continuous variables ; 3.3. Data exploration: categorical variable ; 3.4. SAS macro applications used in data exploration ; 3.5. Summary -- 4 Unsupervised learning methods: 4.1. Introduction ; 4.2. Applications of unsupervised learning methods ; 4.3. Principal component analysis ; 4.4. Exploratory factor analysis ; 4.5. Disjoint cluster analysis ; 4.6. Biplot display of PCA, EFA, and DCS results ; 4.7. PCA and EFA using SAS macro factor2 ; 4.8. Disjoint cluster analysis using SAS macro DISJCLS2 ; 4.9. Summary -- 5 Supervised learning methods: prediction: 5.1. Introduction ; 5.2. Applications of supervised predictive methods ; 5.3. Multiple linear regression modeling ; 5.4. Binary logistic regression modeling ; 5.5. Ordinal logistic regression ; 5.6. Survey logistic regression ; 5.7. Multiple linear regression using SAS macro REGDIAG2 ; 5.8. Lift chart using SAS macro LIFT2 ; 5.9. Scoring new regression data using the SAS macro RSCORE2 ; 5.10. Logistic regression using SAS macro LOGIST2 ; 5.11. Scoring new logistic regression data using the SAS macro LSCORE2 ; 5.12 Case study 1: Modeling multiple linear regressions ; 5.13. Case study 2: If-then analysis and lift charts ; 5.14. Case study 3: Modeling multiple linear regression ; 5.15. Case study 4: Modeling binary logistic regression ; 5.16. Case study: 5 modeling binary multiple logistic regression ; 5.17. Case study: 6 Modeling ordinal multiple logistic regression ; 5.18. Summary -- 6. Supervised learning methods: classification: 6.1. Introduction ; 6.2. Discriminant analysis ; 6.3. Stepwise discriminant analysis ; 6.4. Canonical discriminant analysis ; 6.5. Discriminant function analysis ; 6.6. Applications of discriminant analysis ; 6.7. Classification tree based on CHAID ; 6.8. Applications of CHAID ; 6.9. Discriminant analysis using SAS macro DISCRIM2 ; 6.10. Decision tree using SAS macro CHAID2 ; 6.11. Case study 1_ Canonical discriminant analysis and parametric discriminant function analysis ; 6.12. Case study 2: Nonparametric discriminant function analysis ; 6.13. Case study 3: Classification tree using CHAID ; 6.14. Summary -- 7. Advanced analytics and other SAS data mining resources: 7.1. Introduction ; 7.2. Artificial neural network methods ; 7.3. Market basket analysis ; 7.4. SAS software: the leader in data mining ; 7.5. Summary -- Appendix I: Instruction for using the SAS macros -- Appendix II: Data mining SAS macro help files -- Appendix III: Instruction for using the SAS macros with enterprise guide code window.
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Preface -- Acknowledgments ; About the author -- 1. Data mining: a gentle introduction: 1.1. Introduction ; 1.2. Data mining: why it is successful in the IT world ; 1.3. Benefits of data mining ; 1.4. Data mining: users ; 1.5. Data mining: tools ; 1.6. Data mining: steps ; 1.7. Problems in the data mining process ; 1.8. SAS Software the leader in data mining ; 1.9. Introduction of User-friendly SAS macros for statistical ; 1.10. Summary -- 2. Preparing data for data mining: 2.1. Introduction ; 2.2. Data requirements in data mining ; 2.3. Ideal structures of data for data mining ; 2.4. Understanding the measurement scale of variables ; 2.5. Entire database or representative sample ; 2.6. Sampling for data mining ; 2.7. User-friendly SAS applications used in data preparation ; 2.8. Summary -- 3 Exploratory data analysis: 3.1. Introduction ; 3.2. Exploring continuous variables ; 3.3. Data exploration: categorical variable ; 3.4. SAS macro applications used in data exploration ; 3.5. Summary -- 4 Unsupervised learning methods: 4.1. Introduction ; 4.2. Applications of unsupervised learning methods ; 4.3. Principal component analysis ; 4.4. Exploratory factor analysis ; 4.5. Disjoint cluster analysis ; 4.6. Biplot display of PCA, EFA, and DCS results ; 4.7. PCA and EFA using SAS macro factor2 ; 4.8. Disjoint cluster analysis using SAS macro DISJCLS2 ; 4.9. Summary -- 5 Supervised learning methods: prediction: 5.1. Introduction ; 5.2. Applications of supervised predictive methods ; 5.3. Multiple linear regression modeling ; 5.4. Binary logistic regression modeling ; 5.5. Ordinal logistic regression ; 5.6. Survey logistic regression ; 5.7. Multiple linear regression using SAS macro REGDIAG2 ; 5.8. Lift chart using SAS macro LIFT2 ; 5.9. Scoring new regression data using the SAS macro RSCORE2 ; 5.10. Logistic regression using SAS macro LOGIST2 ; 5.11. Scoring new logistic regression data using the SAS macro LSCORE2 ; 5.12 Case study 1: Modeling multiple linear regressions ; 5.13. Case study 2: If-then analysis and lift charts ; 5.14. Case study 3: Modeling multiple linear regression ; 5.15. Case study 4: Modeling binary logistic regression ; 5.16. Case study: 5 modeling binary multiple logistic regression ; 5.17. Case study: 6 Modeling ordinal multiple logistic regression ; 5.18. Summary -- 6. Supervised learning methods: classification: 6.1. Introduction ; 6.2. Discriminant analysis ; 6.3. Stepwise discriminant analysis ; 6.4. Canonical discriminant analysis ; 6.5. Discriminant function analysis ; 6.6. Applications of discriminant analysis ; 6.7. Classification tree based on CHAID ; 6.8. Applications of CHAID ; 6.9. Discriminant analysis using SAS macro DISCRIM2 ; 6.10. Decision tree using SAS macro CHAID2 ; 6.11. Case study 1_ Canonical discriminant analysis and parametric discriminant function analysis ; 6.12. Case study 2: Nonparametric discriminant function analysis ; 6.13. Case study 3: Classification tree using CHAID ; 6.14. Summary -- 7. Advanced analytics and other SAS data mining resources: 7.1. Introduction ; 7.2. Artificial neural network methods ; 7.3. Market basket analysis ; 7.4. SAS software: the leader in data mining ; 7.5. Summary -- Appendix I: Instruction for using the SAS macros -- Appendix II: Data mining SAS macro help files -- Appendix III: Instruction for using the SAS macros with enterprise guide code window.

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