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Data mining and predictive analytics /
"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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons,
[2015]
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| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Series; Title Page; Copyright; Table of Contents; Dedication; Preface; What is Data Mining? What is Predictive Analytics?; Why is this Book Needed?; Who Will Benefit from this Book?; Danger! Data Mining is Easy to do Badly; "White-Box" Approach; Algorithm Walk-Throughs; Exciting New Topics; The R Zone; Appendix: Data Summarization and Visualization; The Case Study: Bringing it all Together; How the Book is Structured; The Software; Weka: The Open-Source Alternative; The Companion Web Site: www.dataminingconsultant.com; Data Mining and Predictive Analytics as a Textbook; Acknowledgments.
- Daniel's AcknowledgmentsChantal's Acknowledgments; Part I: Data Preparation; Chapter 1: An Introduction to Data Mining and Predictive Analytics; 1.1 What is Data Mining? What Is Predictive Analytics?; 1.2 Wanted: Data Miners; 1.3 The Need For Human Direction of Data Mining; 1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM; 1.5 Fallacies of Data Mining; 1.6 What Tasks can Data Mining Accomplish; The R Zone; R References; Exercises; Chapter 2: Data Preprocessing; 2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data.
- 2.4 Identifying Misclassifications2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables; 2.17 Adding an Index Field; 2.18 Removing Variables that are not Useful; 2.19 Variables that Should Probably not be Removed.
- 2.20 Removal of Duplicate Records2.21 A Word About ID Fields; The R Zone; R Reference; Exercises; Chapter 3: Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know The Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields; 3.8 Binning Based on Predictive Value; 3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables.
- 3.11 Using EDA to Investigate Correlated Predictor Variables3.12 Summary of Our EDA; The R Zone; R References; Exercises; Chapter 4: Dimension-Reduction Methods; 4.1 Need for Dimension-Reduction in Data Mining; 4.2 Principal Components Analysis; 4.3 Applying PCA to the Houses Data Set; 4.4 How Many Components Should We Extract?; 4.5 Profiling the Principal Components; 4.6 Communalities; 4.7 Validation of the Principal Components; 4.8 Factor Analysis; 4.9 Applying Factor Analysis to the Adult Data Set; 4.10 Factor Rotation; 4.11 User-Defined Composites.