| Edition |
Second edition. |
| Description |
xxix, 794 pages : illustrations ; 25 cm. |
| Series |
Wiley series on methods and applications in data mining |
|
Wiley series on methods and applications in data mining.
|
| Bibliography |
Includes bibliographical references and index. |
| 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. |
| Summary |
Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified "white box" approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. |
| Subject |
Data mining.
|
|
Prediction theory.
|
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Manuels.
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Fouille de données.
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Méthodes statistiques.
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Etudes de cas.
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Data mining. (OCoLC)fst00887946
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Prediction theory. (OCoLC)fst01075037
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| Added Author |
Larose, Chantal D.
|
| ISBN |
9781118116197 (cloth) |
|
1118116194 (cloth) |
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