Includes index.

Includes bibliographical references and index.

DISCOVERING KNOWLEDGE IN DATA -- Contents -- Preface -- 1 An Introduction to Data Mining -- 1.1 What is Data Mining? -- 1.2 Wanted: Data Miners -- 1.3 The Need for Human Direction of Data Mining -- 1.4 The Cross-Industry Standard Practice for Data Mining -- 1.4.1 Crisp-DM: The Six Phases -- 1.5 Fallacies of Data Mining -- 1.6 What Tasks Can Data Mining Accomplish? -- 1.6.1 Description -- 1.6.2 Estimation -- 1.6.3 Prediction -- 1.6.4 Classification -- 1.6.5 Clustering -- 1.6.6 Association -- References -- Exercises -- 2 Data Preprocessing -- 2.1 Why do We Need to Preprocess the Data? -- 2.2 Data Cleaning -- 2.3 Handling Missing Data -- 2.4 Identifying Misclassifications -- 2.5 Graphical Methods for Identifying Outliers -- 2.6 Measures of Center and Spread -- 2.7 Data Transformation -- 2.8 Min-Max Normalization -- 2.9 Z-Score Standardization -- 2.10 Decimal Scaling -- 2.11 Transformations to Achieve Normality -- 2.12 Numerical Methods for Identifying Outliers -- 2.13 Flag Variables -- 2.14 Transforming Categorical Variables into Numerical Variables -- 2.15 Binning Numerical Variables -- 2.16 Reclassifying Categorical Variables -- 2.17 Adding an Index Field -- 2.18 Removing Variables that are Not Useful -- 2.19 Variables that Should Probably Not Be Removed -- 2.20 Removal of Duplicate Records -- 2.21 A Word About Id Fields -- THE R ZONE -- References -- Exercises -- Hands-On Analysis -- 3 Exploratory Data Analysis -- 3.1 Hypothesis Testing Versus Exploratory Data Analysis -- 3.2 Getting to Know the Data Set -- 3.3 Exploring Categorical Variables -- 3.4 Exploring Numeric Variables -- 3.5 Exploring Multivariate Relationships -- 3.6 Selecting Interesting Subsets of the Data for Further Investigation -- 3.7 Using EDA to Uncover Anomalous Fields -- 3.8 Binning Based on Predictive Value -- 3.9 Deriving New Variables: Flag Variables.

3.10 Deriving New Variables: Numerical Variables -- 3.11 Using EDA to Investigate Correlated Predictor Variables -- 3.12 Summary -- THE R ZONE -- Reference -- Exercises -- Hands-On Analysis -- 4 Univariate Statistical Analysis -- 4.1 Data Mining Tasks in Discovering Knowledge in Data -- 4.2 Statistical Approaches to Estimation and Prediction -- 4.3 Statistical Inference -- 4.4 How Confident are We in Our Estimates? -- 4.5 Confidence Interval Estimation of the Mean -- 4.6 How to Reduce the Margin of Error -- 4.7 Confidence Interval Estimation of the Proportion -- 4.8 Hypothesis Testing for the Mean -- 4.9 Assessing the Strength of Evidence Against the Null Hypothesis -- 4.10 Using Confidence Intervals to Perform Hypothesis Tests -- 4.11 Hypothesis Testing for the Proportion -- THE R ZONE -- Reference -- Exercises -- 5 Multivariate Statistics -- 5.1 Two-Sample t-Test for Difference in Means -- 5.2 Two-Sample Z-Test for Difference in Proportions -- 5.3 Test for Homogeneity of Proportions -- 5.4 Chi-Square Test for Goodness of Fit of Multinomial Data -- 5.5 Analysis of Variance -- 5.6 Regression Analysis -- 5.7 Hypothesis Testing in Regression -- 5.8 Measuring the Quality of a Regression Model -- 5.9 Dangers of Extrapolation -- 5.10 Confidence Intervals for the Mean Value of Given -- 5.11 Prediction Intervals for a Randomly Chosen Value of Given -- 5.12 Multiple Regression -- 5.13 Verifying Model Assumptions -- THE R ZONE -- Reference -- Exercises -- Hands-On Analysis -- 6 Preparing to Model the Data -- 6.1 Supervised Versus Unsupervised Methods -- 6.2 Statistical Methodology and Data Mining Methodology -- 6.3 Cross-Validation -- 6.4 Overfitting -- 6.5 BIAS-Variance Trade-Off -- 6.6 Balancing the Training Data Set -- 6.7 Establishing Baseline Performance -- THE R ZONE -- Reference -- Exercises -- 7 k-Nearest Neighbor Algorithm.

11.2 Kohonen Networks -- 11.2.1 Kohonen Networks Algorithm -- 11.3 Example of a Kohonen Network Study -- 11.4 Cluster Validity -- 11.5 Application of Clustering Using Kohonen Networks -- 11.6 Interpreting the Clusters -- 11.6.1 Cluster Profiles -- 11.7 Using Cluster Membership as Input to Downstream Data Mining Models -- THE R ZONE -- References -- Exercises -- Hands-On Analysis -- 12 Association Rules -- 12.1 Affinity Analysis and Market Basket Analysis -- 12.1.1 Data Representation for Market Basket Analysis -- 12.2 Support, Confidence, Frequent Itemsets, and the a Priori Property -- 12.3 How Does the a Priori Algorithm Work? -- 12.3.1 Generating Frequent Itemsets -- 12.3.2 Generating Association Rules -- 12.4 Extension from Flag Data to General Categorical Data -- 12.5 Information-Theoretic Approach: Generalized Rule Induction Method -- 12.5.1 J-Measure -- 12.6 Association Rules are Easy to do Badly -- 12.7 How can we Measure the Usefulness of Association Rules? -- 12.8 Do Association Rules Represent Supervised or Unsupervised Learning? -- 12.9 Local Patterns Versus Global Models -- THE R ZONE -- References -- Exercises -- Hands-On Analysis -- 13 Imputation of Missing Data -- 13.1 Need for Imputation of Missing Data -- 13.2 Imputation of Missing Data: Continuous Variables -- 13.3 Standard Error of the Imputation -- 13.4 Imputation of Missing Data: Categorical Variables -- 13.5 Handling Patterns in Missingness -- THE R ZONE -- Reference -- Exercises -- Hands-On Analysis -- 14 Model Evaluation Techniques -- 14.1 Model Evaluation Techniques for the Description Task -- 14.2 Model Evaluation Techniques for the Estimation and Prediction Tasks -- 14.3 Model Evaluation Techniques for the Classification Task -- 14.4 Error Rate, False Positives, and False Negatives -- 14.5 Sensitivity and Specificity.

14.6 Misclassification Cost Adjustment to Reflect Real-World Concerns -- 14.7 Decision Cost/Benefit Analysis -- 14.8 Lift Charts and Gains Charts -- 14.9 Interweaving Model Evaluation with Model Building -- 14.10 Confluence of Results: Applying a Suite of Models -- THE R ZONE -- Reference -- Exercises -- Hands-On Analysis -- Appendix Data Summarization and Visualization -- Part 1 Summarization 1: Building Blocks of Data Analysis -- Part 2 Visualization: Graphs and Tables for Summarizing and Organizing Data -- 2.1 Categorical Variables -- 2.2 Quantitative Variables -- Part 3 Summarization 2: Measures of Center, Variability, and Position -- Part 4 Summarization and Visualization of Bivariate Relationships -- Index.

Copyright © John Wiley & Sons 2014

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