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| Data Mining Methods And Models |
Department of Mathematical Sciences
Central Connecticut State University
WHAT IS DATA MINING?
Data mining is the analysis of (often large) observational data sets to find
unsuspected relationships and to summarize the data in novel ways that are both
understandable and useful to the data owner.
—David Hand, Heikki Mannila, and Padhraic Smyth, Principles of Data Mining,
MIT Press, Cambridge, MA, 2001
Data mining is predicted to be “one of the most revolutionary developments of the
next decade,” according to the online technology magazine ZDNET News (February
8, 2001). In fact, the MIT Technology Review chose data mining as one of 10 emerging
technologies that will change the world.
Because data mining represents such an important field, Wiley-Interscience
and I have teamed up to publish a new series on data mining, initially consisting of
three volumes. The first volume in this series, Discovering Knowledge in Data: An
Introduction to Data Mining, appeared in 2005 and introduced the reader to this rapidly
growing field. The second volume in the series, Data Mining Methods and Models,
explores the process of data mining from the point of view of model building: the
development of complex and powerful predictive models that can deliver actionable
results for a wide range of business and research problems.
WHY IS THIS BOOK NEEDED?
Data Mining Methods and Models continues the thrust of Discovering Knowledge in
Data, providing the reader with:
Models and techniques to uncover hidden nuggets of information
Insight into how the data mining algorithms really work
Experience of actually performing data mining on large data sets
Contents
PREFACE
1 DIMENSION REDUCTION METHODS
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
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
t-Test for the Relationship Between x and y 58
Confidence Interval for the Slope of the Regression Line 60
Confidence Interval for the Mean Value of y Given x 60
Prediction Interval for a Randomly Chosen Value of y 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
3 MULTIPLE REGRESSION AND MODEL BUILDING
Example of Multiple Regression 93
Multiple Regression Model 99
Inference in Multiple Regression 100
t-Test for the Relationship Between y and xi 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 y Given x1, x2, . . ., xm 105
Prediction Interval for a Randomly Chosen Value of y Given x1, x2, . . ., xm 105
Regression with Categorical Predictors 105
Adjusting R2: 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
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
References 199
Exercises 199
5 NAIVE BAYES ESTIMATION AND BAYESIAN NETWORKS
Bayesian Approach 204
Maximum a Posteriori Classification 206
Posterior Odds Ratio 210
Balancing the Data 212
Na˙ıve 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
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
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
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-PCA Models 306
Combining Models Using the Mean Response Probabilities 308
Summary 312
References 316
INDEX 317
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Product details
Price
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File Size
| 6,369 KB |
Pages
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340 p |
File Type
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PDF format |
ISBN-13
ISBN-10 | 978-0-471-66656-1 0-471-66656-4 (cloth) |
Copyright
|
2006 by John Wiley & Sons, Inc
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