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Data Mining for Business Intelligence : Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

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품목정보
출간일 2006년 12월 11일
쪽수, 무게, 크기 279쪽 | 670g | 177*260*20mm
ISBN13 9780470084854
ISBN10 0470084855

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Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence
In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology's Sloan School of Management, and the University of Maryland's Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models.
Featuring XLMiner®, the Microsoft Office Excel® add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of data mining techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding.
Data Mining for Business Intelligence:
Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis
Features a business decision-making context for these key methods
Illustrates the application and interpretation of these methods using real business cases and data
This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiserbusiness decisions.

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Foreword
Preface
Acknowledgments
Introduction
What Is Data Mining?
Where Is Data Mining Used?
The Origins of Data Mining
The Rapid Growth of Data Mining
Why Are There So Many Different Methods? 4
Terminology and Notation 4
Road Maps to This Book 6
Overview of the Data Mining Process 9
Introduction 9
Core Ideas in Data Mining 9
Supervised and Unsupervised Learning 11
The Steps in Data Mining 11
Preliminary Steps 13
Building a Model: Example with Linear Regression 21
Using Excel for Data Mining 27
Problems 31
Data Exploration and Dimension Reduction 35
Introduction 35
Practical Considerations 35
House Prices in Boston 36
Data Summaries 37
Data Visualization 38
Correlation Analysis 40
Reducing the Number of Categories in Categorical Variables 41
Principal Components Analysis 41
Breakfast Cereals 42
Principal Components 45
Normalizing the Data 46
Using Principal Components for Classification and Prediction 49
Problems 51
Evaluating Classification and Predictive Performance 53
Introduction 53
Judging Classification Performance 53
Accuracy Measures 53
Cutoff for Classification 56
Performance in Unequal Importance of Classes 60
Asymmetric Misclassification Costs 61
Oversampling and Asymmetric Costs 66
Classification Using a Triage Strategy 72
Evaluating Predictive Performance 72
Problems 74
Multiple Linear Regression 75
Introduction 75
Explanatory vs. Predictive Modeling 76
Estimating the Regression Equation and Prediction 76
Example: Predicting the Price of Used Toyota Corolla Automobiles 77
Variable Selection in Linear Regression 81
Reducing the Number of Predictors 81
How to Reduce the Number of Predictors 82
Problems 86
Three Simple Classification Methods 91
Introduction 91
Predicting Fraudulent Financial Reporting 91
Predicting Delayed Flights 92
The Naive Rule 92
Naive Bayes 93
Conditional Probabilities and Pivot Tables 94
A Practical Difficulty 94
A Solution: Naive Bayes 95
Advantages and Shortcomings of the naive Bayes Classifier 100
k-Nearest Neighbors 103
Riding Mowers 104
Choosing k 105
k-NN for a Quantitative Response 106
Advantages and Shortcomings of k-NN Algorithms 106
Problems 108
Classification and Regression Trees 111
Introduction 111
Classification Trees 113
Recursive Partitioning 113
Example 1: Riding Mowers 113
Measures of Impurity 115
Evaluating the Performance of a Classification Tree 120
Acceptance of Personal Loan 120
Avoiding Overfitting 121
Stopping Tree Growth: CHAID 121
Pruning the Tree 125
Classification Rules from Trees 130
Regression Trees 130
Prediction 130
Measuring Impurity 131
Evaluating Performance 132
Advantages, Weaknesses, and Extensions 132
Problems 134
Logistic Regression 137
Introduction 137
The Logistic Regression Model 138
Example: Acceptance of Personal Loan 139
Model with a Single Predictor 141
Estimating the Logistic Model from Data: Computing Parameter Estimates 143
Interpreting Results in Terms of Odds 144
Why Linear Regression Is Inappropriate for a Categorical Response 146
Evaluating Classification Performance 148
Variable Selection 148
Evaluating Goodness of Fit 150
Example of Complete Analysis: Predicting Delayed Flights 153
Data Preprocessing 154
Model Fitting and Estimation 155
Model Interpretation 155
Model Performance 155
Goodness of fit 157
Variable Selection 158
Logistic Regression for More Than Two Classes 160
Ordinal Classes 160
Nominal Classes 161
Problems 163
Neural Nets 167
Introduction 167
Concept and Structure of a Neural Network 168
Fitting a Network to Data 168
Tiny Dataset 169
Computing Output of Nodes 170
Preprocessing the Data 172
Training the Model 172
Classifying Accident Severity 176
Avoiding overfitting 177
Using the Output for Prediction and Classification 181
Required User Input 181
Exploring the Relationship Between Predictors and Response 182
Advantages and Weaknesses of Neural Networks 182
Problems 184
Discriminant Analysis 187
Introduction 187
Example 1: Riding Mowers 187
Example 2: Personal Loan Acceptance 188
Distance of an Observation from a Class 188
Fisher's Linear Classification Functions 191
Classification Performance of Discriminant Analysis 194
Prior Probabilities 195
Unequal Misclassification Costs 195
Classifying More Than Two Classes 196
Medical Dispatch to Accident Scenes 196
Advantages and Weaknesses 197
Problems 200
Association Rules 203
Introduction 203
Discovering Association Rules in Transaction Databases 203
Example 1: Synthetic Data on Purchases of Phone Faceplates 204
Generating Candidate Rules 204
The Apriori Algorithm 205
Selecting Strong Rules 206
Support and Confidence 206
Lift Ratio 207
Data Format 207
The Process of Rule Selection 209
Interpreting the Results 210
Statistical Significance of Rules 211
Example 2: Rules for Similar Book Purchases 212
Summary 212
Problems 215
Cluster Analysis 219
Introduction 219
Example: Public Utilities 220
Measuring Distance Between Two Records 222
Euclidean Distance 223
Normalizing Numerical Measurements 223
Other Distance Measures for Numerical Data 223
Distance Measures for Categorical Data 226
Distance Measures for Mixed Data 226
Measuring Distance Between Two Clusters 227
Hierarchical (Agglomerative) Clustering 228
Minimum Distance (Single Linkage) 229
Maximum Distance (Complete Linkage) 229
Group Average (Average Linkage) 230
Dendrograms: Displaying Clustering Process and Results 230
Validating Clusters 231
Limitations of Hierarchical Clustering 232
Nonhierarchical Clustering: The k-Means Algorithm 233
Initial Partition into k Clusters 234
Problems 237
Cases 241
Charles Book Club 241
German Credit 250
Tayko Software Cataloger 254
Segmenting Consumers of Bath Soap 258
Direct-Mail Fundraising 262
Catalog Cross-Selling 265
Predicting Bankruptcy 267
References 271
Index 273
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