Discovering Knowledge in Data: An Introduction to Data Mining

DISCOVERING KNOWLEDGE IN DATA......Page 3
 CONTENTS......Page 9
 PREFACE......Page 13
 1 INTRODUCTION TO DATA MINING......Page 19
  What Is Data Mining?......Page 20
  Need for Human Direction of Data Mining......Page 22
  Cross-Industry Standard Process: CRISP–DM......Page 23
  Case Study 1: Analyzing Automobile Warranty Claims: Example of the CRISP–DM Industry Standard Process in Action......Page 26
  Fallacies of Data Mining......Page 28
   Description......Page 29
   Estimation......Page 30
   Prediction......Page 31
   Classification......Page 32
   Clustering......Page 34
   Association......Page 35
  Case Study 2: Predicting Abnormal Stock Market Returns Using Neural Networks......Page 36
  Case Study 3: Mining Association Rules from Legal Databases......Page 37
  Case Study 4: Predicting Corporate Bankruptcies Using Decision Trees......Page 39
  Case Study 5: Profiling the Tourism Market Using k-Means Clustering Analysis......Page 41
  References......Page 42
  Exercises......Page 43
  Why Do We Need to Preprocess the Data?......Page 45
  Data Cleaning......Page 46
  Handling Missing Data......Page 48
  Identifying Misclassifications......Page 51
  Graphical Methods for Identifying Outliers......Page 52
  Data Transformation......Page 53
   Min–Max Normalization......Page 54
   Z-Score Standardization......Page 55
  Numerical Methods for Identifying Outliers......Page 56
  Exercises......Page 57
  Hypothesis Testing versus Exploratory Data Analysis......Page 59
  Getting to Know the Data Set......Page 60
  Dealing with Correlated Variables......Page 62
  Exploring Categorical Variables......Page 63
  Using EDA to Uncover Anomalous Fields......Page 68
  Exploring Numerical Variables......Page 70
  Exploring Multivariate Relationships......Page 77
  Selecting Interesting Subsets of the Data for Further Investigation......Page 79
  Binning......Page 80
  Summary......Page 81
  Exercises......Page 82
  Data Mining Tasks in Discovering Knowledge in Data......Page 85
  Statistical Approaches to Estimation and Prediction......Page 86
  Univariate Methods: Measures of Center and Spread......Page 87
  Statistical Inference......Page 89
  Confidence Interval Estimation......Page 91
  Bivariate Methods: Simple Linear Regression......Page 93
  Dangers of Extrapolation......Page 97
  Prediction Intervals for a Randomly Chosen Value of y Given x......Page 98
  Multiple Regression......Page 101
  Verifying Model Assumptions......Page 103
  Exercises......Page 106
  Supervised versus Unsupervised Methods......Page 108
  Methodology for Supervised Modeling......Page 109
  Bias–Variance Trade-Off......Page 111
  Classification Task......Page 113
  k-Nearest Neighbor Algorithm......Page 114
  Distance Function......Page 117
   Simple Unweighted Voting......Page 119
   Weighted Voting......Page 120
  Quantifying Attribute Relevance: Stretching the Axes......Page 121
  k-Nearest Neighbor Algorithm for Estimation and Prediction......Page 122
  Choosing k......Page 123
  Exercises......Page 124
 6 DECISION TREES......Page 125
  Classification and Regression Trees......Page 127
  C4.5 Algorithm......Page 134
  Decision Rules......Page 139
  Comparison of the C5.0 and CART Algorithms Applied to Real Data......Page 140
  Exercises......Page 144
 7 NEURAL NETWORKS......Page 146
  Input and Output Encoding......Page 147
  Simple Example of a Neural Network......Page 149
  Sigmoid Activation Function......Page 152
  Gradient Descent Method......Page 153
  Back-Propagation Rules......Page 154
  Example of Back-Propagation......Page 155
  Learning Rate......Page 157
  Momentum Term......Page 158
  Sensitivity Analysis......Page 160
  Application of Neural Network Modeling......Page 161
  Exercises......Page 163
  Clustering Task......Page 165
  Hierarchical Clustering Methods......Page 167
   Single-Linkage Clustering......Page 168
   Complete-Linkage Clustering......Page 169
  Example of k-Means Clustering at Work......Page 171
  Application of k-Means Clustering Using SAS Enterprise Miner......Page 176
  References......Page 179
  Exercises......Page 180
  Self-Organizing Maps......Page 181
  Kohonen Networks......Page 183
  Example of a Kohonen Network Study......Page 184
  Application of Clustering Using Kohonen Networks......Page 188
   Interpreting the Clusters......Page 189
   Cluster Profiles......Page 193
  Using Cluster Membership as Input to Downstream Data Mining Models......Page 195
  Exercises......Page 196
  Affinity Analysis and Market Basket Analysis......Page 198
   Data Representation for Market Basket Analysis......Page 200
  Support, Confidence, Frequent Itemsets, and the A Priori Property......Page 201
  How Does the A Priori Algorithm Work (Part 1)? Generating Frequent Itemsets......Page 203
  How Does the A Priori Algorithm Work (Part 2)? Generating Association Rules......Page 204
  Extension from Flag Data to General Categorical Data......Page 207
   J-Measure......Page 208
   Application of Generalized Rule Induction......Page 209
  When Not to Use Association Rules......Page 211
  Do Association Rules Represent Supervised or Unsupervised Learning?......Page 214
  Local Patterns versus Global Models......Page 215
  Exercises......Page 216
 11 MODEL EVALUATION TECHNIQUES......Page 218
  Model Evaluation Techniques for the Estimation and Prediction Tasks......Page 219
  Error Rate, False Positives, and False Negatives......Page 221
  Misclassification Cost Adjustment to Reflect Real-World Concerns......Page 223
  Decision Cost/Benefit Analysis......Page 225
  Lift Charts and Gains Charts......Page 226
  Interweaving Model Evaluation with Model Building......Page 229
  Confluence of Results: Applying a Suite of Models......Page 230
  Exercises......Page 231
 EPILOGUE: “WE’VE ONLY JUST BEGUN”......Page 233
 INDEX......Page 235