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Bibliografická citace

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BK
Hoboken : John Wiley & Sons, c2006
xvi,322 s. : il.

ISBN 0-471-66656-4 (váz.)
Obsahuje ilustrace, fotografie, grafy, bibliografické odkazy, rejstřík
Data - dolování - učebnice vysokošk.
000019592
CONTENTS // PREFACE Xi // 1 DIMENSION REDUCTION METHODS i // Need for Dimension Reduction in Data Mining 1 // Principal Components Analysis 2 // Applying Principal Components Analysis to the Houses Data Set 5 // How Many Components Should We Extract? 9 // Profiling the Principal Components 13 // Communalities 15 // Validation of the Principal Components 17 // Factor Analysis 18 // Applying Factor Analysis to the Adult Data Set 18 // Factor Rotation 20 // User-Defined Composites 23 // Example of a User-Defined Composite 24 // Summary 25 // References 28 // Exercises 28 // 2 REGRESSION MODELING 33 // Example of Simple Linear Regression 34 // Least-Squares Estimates 36 // Coefficient of Determination 39 // Standard Error of the Estimate 43 // Correlation Coefficient 45 // ANOVA Table 46 // Outliers, High Leverage Points, and Influential Observations 48 // Regression Model 55 // Inference in Regression 57 // r-Test for the Relationship Between x and ? 58 // Confidence Interval for the Slope of the Regression Line 60 // Confidence Interval for the Mean Value of ? Given x 60 // Prediction Interval for a Randomly Chosen Value of ? Given x 61 // Verifying the Regression Assumptions 63 // Example: Baseball Data Set 68 // Example: California Data Set 74 // Transformations to Achieve Linearity 79 // Box-Cox Transformations 83 // Summary 84 // References 86 // Exercises 86 // VII // Vili CONTENTS // 3 MULTIPLE REGRESSION AND MODEL BUILDING 93 // Example of Multiple Regression 93 // Multiple
Regression Model 99 // Inference in Multiple Regression 100 // r-Test for the Relationship Between  and jc, 101 // F-Test for the Significance of the Overall Regression Model 102 // Confidence Interval for a Particular Coefficient 104 // Confidence Interval for the Mean Value of ? Given Jti, *2,..*m 105 // Prediction Interval for a Randomly Chosen Value of ? Given *1, *2,..JCm 105 // Regression with Categorical Predictors 105 // Adjusting R1: Penalizing Models for Including Predictors That Are // Not Useful 113 // Sequential Sums of Squares 115 // Multicollinearity 116 // Variable Selection Methods 123 // Partial F-Test 123 // Forward Selection Procedure 125 // Backward Elimination Procedure 125 // Stepwise Procedure 126 // Best Subsets Procedure 126 // All-Possible-Subsets Procedure 126 // Application of the Variable Selection Methods 127 // Forward Selection Procedure Applied to the Cereals Data Set 127 // Backward Elimination Procedure Applied to the Cereals Data Set 129 // Stepwise Selection Procedure Applied to the Cereals Data Set 131 // Best Subsets Procedure Applied to the Cereals Data Set 131 // Mallows’ Cp Statistic 131 // Variable Selection Criteria 135 // Using the Principal Components as Predictors 142 // Summary 147 // References 149 // Exercises 149 // 4 LOGISTIC REGRESSION___155 // Simple Example of Logistic Regression 156 // Maximum Likelihood Estimation 158 // Interpreting Logistic Regression Output 159 // Inference: Are the Predictors Significant? 160
Interpreting a Logistic Regression Model 162 // Interpreting a Model for a Dichotomous Predictor 163 // Interpreting a Model for a Polychotomous Predictor 166 // Interpreting a Model for a Continuous Predictor 170 // Assumption of Linearity 174 // Zero-Cell Problem 177 // Multiple Logistic Regression 179 // Introducing Higher-Order Terms to Handle Nonlinearity 183 // Validating the Logistic Regression Model 189 // WEKA: Hands-on Analysis Using Logistic Regression 194 // Summary 197 // CONTENTS ix // References 199 // Exercises 199 // 5 NAIVE BAYES ESTIMATION AND BAYESIAN NETWORKS 204 // Bayesian Approach 204 // Maximum a Posteriori Classification 206 // Posterior Odds Ratio 210 // Balancing the Data 212 // Naive Bayes Classification 215 // Numeric Predictors 219 // WEKA: Hands-on Analysis Using Naive Bayes 223 // Bayesian Belief Networks 227 // Clothing Purchase Example 227 // Using the Bayesian Network to Find Probabilities 229 // WEKA: Hands-On Analysis Using the Bayes Net Classifier 232 // Summary 234 // References 236 // Exercises 237 // 6 GENETIC ALGORITHMS___240 // Introduction to Genetic Algorithms 240 // Basic Framework of a Genetic Algorithm 241 // Simple Example of a Genetic Algorithm at Work 243 // Modifications and Enhancements: Selection 245 // Modifications and Enhancements: Crossover 247 // Multipoint Crossover 247 // Uniform Crossover 247 // Genetic Algorithms for Real-Valued Variables 248 // Single Arithmetic Crossover 248 // Simple Arithmetic Crossover 248
Whole Arithmetic Crossover 249 // Discrete Crossover 249 // Normally Distributed Mutation 249 // Using Genetic Algorithms to Train a Neural Network 249 // WEKA: Hands-on Analysis Using Genetic Algorithms 252 // Summary 261 // References 262 // Exercises 263 // 7 CASE STUDY: MODELING RESPONSE TO DIRECT MAIL MARKETING 265 // Cross-Industry Standard Process for Data Mining 265 // Business Understanding Phase 267 // Direct Mail Marketing Response Problem 267 // Building the Cost/Benefit Table 267 // Data Understanding and Data Preparation Phases 270 // Clothing Store Data Set 270 // Transformations to Achieve Normality or Symmetry 272 // Standardization and Flag Variables 276 // X CONTENTS // Deriving New Variables 277 // Exploring the Relationships Between the Predictors and the Response 278 // Investigating the Correlation Structure Among the Predictors 286 // Modeling and Evaluation Phases 289 // Principal Components Analysis 292 // Cluster Analysis: BIRCH Clustering Algorithm 294 // Balancing the Training Data Set 298 // Establishing the Baseline Model Performance 299 // Model Collection A: Using the Principal Components 300 // Overbalancing as a Surrogate for Misclassification Costs 302 // Combining Models: Voting 304 // Model Collection B: Non-PC A Models 306 // Combining Models Using the Mean Response Probabilities 308 // Summary 312 // References 316 // INDEX 317

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