Larose D.T. Data Mining Methods and Models

John Wiley & Sons, Inc., 2006. — 339 p. — ISBN: 0471666564, 9780471666561.Data Mining Methods and Models provides:
The latest techniques for uncovering hidden nuggets of information
The insight into how the data mining algorithms actually work
The hands-on experience of performing data mining on large data setsData Mining Methods and Models:
Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing"
Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises
Demonstrates the Clementine data mining software suite, WEKA open-source data mining software, SPSS statistical software, and Minitab statistical software
Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes.With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field.Dimension Reduction Methods
Need for Dimension Reduction in Data Mining
Principal Components Analysis
Factor Analysis
User-Defined CompositesExercises
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 LinearityExercises
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’ C_p Statistic
Variable Selection Criteria
Using the Principal Components as PredictorsExercises
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 RegressionExercises
Naive Bayes Estimation and Bayesian Networks
Bayesian Approach
Maximum a Posteriori Classification
Naive Bayes Classification
WEKA: Hands-on Analysis Using Naive Bayes
Bayesian Belief Networks
WEKA: Hands-On Analysis Using the Bayes Net ClassifierExercises
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 AlgorithmsExercises
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