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| Preface | |
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| Acknowledgments | |
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| List of Figures | |
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| List of Tables | |
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| Introduction | |
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| How to Read This Book? | |
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| A Short Introduction to R | |
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| Starting with R | |
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| R Objects | |
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| Vectors | |
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| Vectorization | |
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| Factors | |
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| Generating Sequences | |
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| Sub-Setting | |
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| Matrices and Arrays | |
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| Lists | |
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| Data Frames | |
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| Creating New Functions | |
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| Objects, Classes, and Methods | |
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| Managing Your Sessions | |
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| A Short Introduction to MySQL | |
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| Predicting Algae Blooms | |
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| Problem Description and Objectives | |
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| Data Description | |
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| Loading the Data into R | |
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| Data Visualization and Summarization | |
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| Unknown Values | |
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| Removing the Observations with Unknown Values | |
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| Filling in the Unknowns with the Most Frequent Values | |
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| Filling in the Unknown Values by Exploring Correlations | |
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| Filling in the Unknown Values by Exploring Similarities between Cases | |
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| Obtaining Prediction Models | |
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| Multiple Linear Regression | |
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| Regression Trees | |
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| Model Evaluation and Selection | |
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| Predictions for the Seven Algae | |
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| Summary | |
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| Predicting Stock Market Returns | |
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| Problem Description and Objectives | |
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| The Available Data | |
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| Handling Time-Dependent Data in R | |
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| Reading the Data from the CSV File | |
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| Getting the Data from the Web | |
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| Reading the Data from a MySQL Database | |
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| Loading the Data into R Running on Windows | |
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| Loading the Data into R Running on Linux | |
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| Defining the Prediction Tasks | |
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| What to Predict? | |
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| Which Predictors? | |
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| The Prediction Tasks | |
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| Evaluation Criteria | |
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| The Prediction Models | |
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| How Will the Training Data Be Used? | |
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| The Modeling Tools | |
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| Artificial Neural Networks | |
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| Support Vector Machines | |
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| Multivariate Adaptive Regression Splines | |
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| From Predictions into Actions | |
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| How Will the Predictions Be Used? | |
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| Trading-Related Evaluation Criteria | |
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| Putting Everything Together: A Simulated Trader | |
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| Model Evaluation and Selection | |
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| Monte Carlo Estimates | |
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| Experimental Comparisons | |
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| Results Analysis | |
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| The Trading System | |
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| Evaluation of the Final Test Data | |
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| An Online Trading System | |
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| Summary | |
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| Detecting Fraudulent Transactions | |
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| Problem Description and Objectives | |
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| The Available Data | |
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| Loading the Data into R | |
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| Exploring the Dataset | |
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| Data Problems | |
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| Unknown Values | |
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| Few Transactions of Some Products | |
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| Defining the Data Mining Tasks | |
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| Different Approaches to the Problem | |
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| Unsupervised Techniques | |
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| Supervised Techniques | |
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| Semi-Supervised Techniques | |
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| Evaluation Criteria | |
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| Precision and Recall | |
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| Lift Charts and Precision/Recall Curves | |
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| Normalized Distance to Typical Price | |
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| Experimental Methodology | |
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| Obtaining Outlier Rankings | |
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| Unsupervised Approaches | |
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| The Modified Box Plot Rule | |
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| Local Outlier Factors (LOF) | |
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| Clustering-Based Outlier Rankings (OR<sub>h</sub>) | |
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| Supervised Approaches | |
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| The Class Imbalance Problem | |
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| Naive Bayes | |
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| AdaBoost | |
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| Semi-Supervised Approaches | |
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| Summary | |
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| Classifying Microarray Samples | |
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| Problem Description and Objectives | |
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| Brief Background on Microarray Experiments | |
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| The ALL Dataset | |
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| The Available Data | |
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| Exploring the Dataset | |
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| Gene (Feature) Selection | |
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| Simple Filters Based on Distribution Properties | |
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| ANOVA Filters | |
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| Filtering Using Random Forests | |
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| Filtering Using Feature Clustering Ensembles | |
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| Predicting Cytogenetic Abnormalities | |
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| Defining the Prediction Task | |
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| The Evaluation Metric | |
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| The Experimental Procedure | |
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| The Modeling Techniques | |
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| Random Forests | |
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| k-Nearest Neighbors | |
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| Comparing the Models | |
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| Summary | |
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| Bibliography | |
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| Subject Index | |
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| Index of Data Mining Topics | |
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| Index of R Functions | |