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Table of Contents.doc

  1. 1. Volume I: Discovering Knowledge in Data: An Introduction to Data Mining Brief Table of Contents • Preface • Chapter 1. An Introduction to Data Mining • Chapter 2. Data Preprocessing • Chapter 3. Exploratory Data Analysis • Chapter 4. Statistical Approaches to Estimation and Prediction • Chapter 5. K-Nearest Neighbor • Chapter 6. Decision Trees • Chapter 7. Neural Networks • Chapter 8. Hierarchical and K-Means Clustering • Chapter 9. Kohonen networks • Chapter 10. Association Rules • Chapter 11. Model Evaluation Techniques • Epilogue Detailed Table of Contents • Preface • Chapter 1. An Introduction to Data Mining o What is Data Mining? o Why Data Mining? o The Need for Human Direction of Data Mining o The Cross-Industry Standard Process CRISP –DM o Case Study 1 : Analyzing Automobile Warranty Claims o Fallacies of Data Mining o What Tasks Can Data Mining Accomplish? o Case Study 2: Predicting Abnormal Stock Market Returns Using Neural Networks o Case Study 3: Mining Association Rules from Legal Databases o Case Study 4: Predicting Corporate Bankruptcies Using Decision Trees o Case Study 5: Profiling the Tourism Market using K-Means Clustering o Chapter 1 Bibliography o Chapter 1 Exercises • Chapter 2. Data Preprocessing o Why Do We Need to Preprocess the Data? o Data Cleaning o Handling Missing Data o Identifying Misclassifications o Graphical Methods for Identifying Outliers o Data Normalization: o Min-Max Normalization o Z-Score Standardization
  2. 2. o Numerical Methods for Identifying Outliers o Using Z-Scores for Identifying Outliers o Robust Detection of Outliers o Chapter 2 Bibliography o Chapter 2 Exercises o Chapter 2 Hands-On Analysis • Chapter 3. Exploratory Data Analysis o Hypothesis Testing vs. Exploratory Data Analysis o EDA: Getting to Know the Data Set o EDA: Dealing with Correlated Variables o EDA: Exploring Categorical Variables o Using EDA to Uncover Anomalous Fields o EDA: Exploring Numeric Variables o EDA: Exploring Multivariate Relationships o EDA: Selecting Interesting Subsets of the Data for Further Investigation o Binning o Chapter 3 Bibliography o Chapter 3 Exercises o Chapter 3 Hands-On Analysis • Chapter 4. Statistical Approaches to Estimation and Prediction o The Data Mining Tasks in Discovering Knowledge in Data o Statistical Approaches to Estimation and Prediction o Univariate Methods: Measures of Center and Spread o Statistical Inference o How Confident Are We in Our Estimates? o Confidence Interval Estimation o Simple Linear Regression o The Dangers of Extrapolation o Confidence Intervals for the Mean Value of y Given x o Prediction Intervals for a Randomly Chosen Value of y Given x o Multiple Regression o Verifying Model Assumptions o Chapter 4 Bibliography o Chapter 4 Exercises o Chapter 4 Hands-On Analysis • Chapter 5. K-Nearest Neighbor o Supervised Learning vs. Unsupervised Learning o A Methodology for Supervised Modeling o The Classification Task o The K-Nearest Neighbor Algorithm o The Distance Function
  3. 3. o The Combination Function o Weighted Voting o Quantifying Attribute Relevance: Stretching the Axes o Database Considerations o K-Nearest Neighbor for Estimation and Prediction o Choosing K o Chapter 5 Bibliography o Chapter 5 Exercises • Chapter 6. Decision Trees o Decision Trees o Classification and Regression Trees o The C4.5 Algorithm o Decision Rules o A Comparison of the C5.0 and CART Algorithms Applied to Real Data o Chapter 6 Bibliography o Chapter 6 Exercises o Chapter 6 Hands-On Analysis • Chapter 7. Neural Networks o Input and Output Encoding o Neural Networks for Estimation and Prediction o A Simple Example of a Neural Network o The Sigmoid Activation Function o Backpropagation o The Gradient Descent Method o The Backpropagation Rules o An Example of Backpropagation o Termination Criteria o The Learning Rate η o The Momentum Term α o Sensitivity Analysis o An Application of Neural Network Modeling o Chapter 7 Bibliography o Chapter 7 Exercises o Chapter 7 Hands-On Analysis • Chapter 8. Hierarchical and K-Means Clustering o The Clustering Task o Hierarchical Clustering Methods o K-Means Clustering o An Application of K-Means Clustering using SAS Enterprise Miner o Using Cluster Membership to Predict Churn o Chapter 8 Bibliography
  4. 4. o Chapter 8 Exercises o Chapter 8 Hands-On Analysis • Chapter 9: Kohonen networks o Self-Organizing Maps o Kohonen Networks o An Example o Cluster Validity o An Application of Clustering Using Kohonen Networks o Interpreting the Clusters o Cluster Profiles o Using Cluster Membership as Input to Downstream Data Mining Models o Chapter 9 Bibliography o Chapter 9 Exercises o Chapter 9 Hands-On Analysis • Chapter 10. Data Mining Techniques: Association Rules o Affinity Analysis and Market Basket Analysis o Data Representation for Market Basket Analysis o Support, Confidence, Frequent Itemsets, and the A Priori Property o How Does the A Priori Algorithm Work (Part 1)? Generating Frequent Itemsets o How Does the A Priori Algorithm Work (Part 2)? Generating Association Rules o The Extension from Flag Data to General Categorical Data o An Information Theoretic Approach: The Generalized Rule Induction Method o The J-Measure o An Application of Generalized Rule Induction o When Not To Use Association Rules o Do Association Rules Represent Supervised or Unsupervised Learning? o Local Patterns vs. Global Models o Chapter 10 Bibliography o Chapter 10 Exercises o Chapter 10 Hands-On Analysis • Chapter 11. Model Evaluation Techniques o Model Evaluation Techniques for the Description Task o Model Evaluation Techniques for the Estimation and Prediction Tasks o Model Evaluation Techniques for the Classification Task o Error Rate, False Positives, and False Negatives o Misclassification Cost Adjustment to Reflect Real-World Concerns o Decision Cost / Benefit Analysis o Lift Charts and Gains Charts
  5. 5. o Interweaving Model Evaluation with Model Building o Confluence of Results: Applying a Suite of Models o Chapter 11 Bibliography o Chapter 11 Exercises o Chapter 11 Hands-On Analysis • Epilogue. We’ve Only Just Begun: An Invitation to Data Mining Methods and Models

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