Includes bibliographical references and index.

1. DIMENSION REDUCTION METHODS. Need for Dimension Reduction in Data Mining. Principal Components Analysis. Factor Analysis. User-Defined Composites -- 2. REGRESSION MODELING. Example of Simple Linear Regression. Least-Squares Estimates. Coefficient of Determination. Standard Error of the Estimate. Correlation Coefficient. ANOVA Table. Outliers, High Leverage Points, and Influential Observations. Regression Model. Inference in Regression. Verifying the Regression Assumptions. Example: Baseball Data Set. Example: California Data Set. Transformations to Achieve Linearity -- 3. MULTIPLE REGRESSION AND MODEL BUILDING. Example of Multiple Regression. Multiple Regression Model. Inference in Multiple Regression. Regression with Categorical Predictors. Multicollinearity. Variable Selection Methods. Application of the Variable Selection Methods. Mallows' Cp Statistic. Variable Selection Criteria. Using the Principal Components as Predictors -- 4. LOGISTIC REGRESSION. Simple Example of Logistic Regression. Maximum Likelihood Estimation. Interpreting Logistic Regression Output. Inference: Are the Predictors Significant?. Interpreting a Logistic Regression Model. Assumption of Linearity. Zero-Cell Problem. Multiple Logistic Regression. Introducing Higher-Order Terms to Handle Nonlinearity. Validating the Logistic Regression Model. WEKA: Hands-on Analysis Using Logistic Regression -- 5. NAIVE BAYES ESTIMATION AND BAYESIAN NETWORKS. Bayesian Approach. Maximum a Posteriori Classification. Na�ive Bayes Classification. WEKA: Hands-on Analysis Using Naive Bayes. Bayesian Belief Networks. WEKA: Hands-On Analysis Using the Bayes Net Classifier -- 6. GENETIC ALGORITHMS. Introduction to Genetic Algorithms. Basic Framework of a Genetic Algorithm. Simple Example of a Genetic Algorithm at Work. Modifications and Enhancements: Selection. Modifications and Enhancements: Crossover. Genetic Algorithms for Real-Valued Variables. Using Genetic Algorithms to Train a Neural Network. WEKA: Hands-on Analysis Using Genetic Algorithms -- 7. CASE STUDY: MODELING RESPONSE TO DIRECT MAIL MARKETING. Cross-Industry Standard Process for Data Mining. Business Understanding Phase. Data Understanding and Data Preparation Phases. Modeling and Evaluation Phases.

Provides an introduction into data mining methods and models, including association rules, clustering, K-nearest neighbor, statistical inference, neural networks, linear and logistic regression, and multivariate analysis.

Print version record and online resource; title from PDF title page (IEEE Xplore, viewed March 14, 2014).

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