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Data mining and predictive analytics/ Daniel T. Larose, Chantal D. Larose

By: Larose, Daniel TContributor(s): Larose, Chantal DSeries: (Wiley series on methods and applications in data mining)Publication details: New Jersey: John Wiley & Sons, c2015Edition: 2nd edDescription: xxix, 794 p. : ill. ; 25 cmISBN: 9781118116197Subject(s): Prediction theory | Data mining | Business--Data processingDDC classification: 006.312

Contents:

Part I. Data Preparation -- Chapter 1. An Introduction to Data Mining and Predictive Analytics -- Chapter 2. Data Preprocessing -- Chapter 3. Exploratory Data Analysis -- Chapter 4. Dimension-Reduction Methods -- Part II. Statistical Analysis -- Chapter 5. Univariate Statistical Analysis -- Chapter 6. Multivariate Statistics -- Chapter 7. Preparing to Model the data -- Chapter 8. Simple Linear Regression -- Chapter 9. Multiple Regression and Model Building -- Part III. Classification -- Chapter 10. k-Nearest Neighbor Algorithm -- Chapter 11. Decision trees -- Chapter 12. Neural Networks -- Chapter 13. Logistic Regression -- Chapter 14. Naïve Bayes and Bayesian Networks -- Chapter 15. Model Evaluation Techniques -- Chapter 16. Cost-Benefit Analysis Using Data-Driven Costs -- Chapter 17. Cost-Benefit Analysis For Trinary and k-Nary Classification Models -- Chapter 18. Graphical Evaluation of Classification Models -- Part IV. Clustering -- Chapter 19. Hierarchical and k-Means Clustering -- Chapter 20. Kohonen Networks --Chapter 21. Birch Clustering-- Chapter 22. Measuring Cluster Goodness -- Part V. Association Rules -- Chapter 23. Association Rules -- Part VI. Enhancing Model Performance -- Chapter 24. Segmentation Models -- Chapter 25. Ensemble Methods: Bagging and Boosting -- Chapter 26. Model Voting and Propensity Averaging -- Part VII. Further Topics -- Chapter 27. Genetic Algorithms -- Chapter 28. Imputation of Missing Data -- Part VIII. Case Study: Predicting Response to Direct-Mail Marketing --Chapter 29. Case Study, Part 1: Business Understanding, Data Preparation, and Eda -- Chapter 30. Case Study, Part 2: Clustering and Principal Components Analysis -- Chapter 31. Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability -- Chapter 32. Case Study, Part 4: Modeling And Evaluation For High Performance Only.

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
General Books	Central Library, Sikkim University General Book Section	006.312 LAR/D (Browse shelf(Opens below))	Available		48047

Total holds: 0

Part I. Data Preparation --
Chapter 1. An Introduction to Data Mining and Predictive Analytics --
Chapter 2. Data Preprocessing --
Chapter 3. Exploratory Data Analysis --
Chapter 4. Dimension-Reduction Methods --
Part II. Statistical Analysis --
Chapter 5. Univariate Statistical Analysis --
Chapter 6. Multivariate Statistics --
Chapter 7. Preparing to Model the data --
Chapter 8. Simple Linear Regression --
Chapter 9. Multiple Regression and Model Building --
Part III. Classification --
Chapter 10. k-Nearest Neighbor Algorithm --
Chapter 11. Decision trees --
Chapter 12. Neural Networks --
Chapter 13. Logistic Regression --
Chapter 14. Naïve Bayes and Bayesian Networks --
Chapter 15. Model Evaluation Techniques --
Chapter 16. Cost-Benefit Analysis Using Data-Driven Costs --
Chapter 17. Cost-Benefit Analysis For Trinary and k-Nary Classification Models --
Chapter 18. Graphical Evaluation of Classification Models --
Part IV. Clustering --
Chapter 19. Hierarchical and k-Means Clustering --
Chapter 20. Kohonen Networks --Chapter 21. Birch Clustering--
Chapter 22. Measuring Cluster Goodness --
Part V. Association Rules --
Chapter 23. Association Rules --
Part VI. Enhancing Model Performance --
Chapter 24. Segmentation Models --
Chapter 25. Ensemble Methods: Bagging and Boosting --
Chapter 26. Model Voting and Propensity Averaging --
Part VII. Further Topics --
Chapter 27. Genetic Algorithms --
Chapter 28. Imputation of Missing Data --
Part VIII. Case Study: Predicting Response to Direct-Mail Marketing --Chapter 29. Case Study, Part 1: Business Understanding, Data Preparation, and Eda --
Chapter 30. Case Study, Part 2: Clustering and Principal Components Analysis --
Chapter 31. Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability --
Chapter 32. Case Study, Part 4: Modeling And Evaluation For High Performance Only.

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