Description
WILEY INDIA Data Mining Methods And Models by Larose Daniel T
About the Book: Data Mining Methods and Models
This book provides in 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. It presents a unified approach based on CRISP methodology (involves Strategic Risk Assessment based on Organizational Modelling).
We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of todays data sets a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis.
Contents
Preface
Dimension Reduction Methods
Need for Dimension Reduction in Data Mining
Principal Components Analysis
Factor Analysis
User-Defined Composites
Regression Modeling
Example of Simple Linear Regression
Least-Squares Estimates
Coefficient or Determination
Correlation Coefficient
The ANOVA Table
Outliers, High Leverage Points, and Influential Observations
The Regression Model
Inference in Regression
Verifying the Regression Assumptions
An Example: The Baseball Data Set
An Example: The California Data Set
Transformations to Achieve Linearity
Multiple Regression and Model Building
An Example of Multiple Regression
The Multiple Regression Model
Inference in Multiple Regression
Regression with Categorical Predictors
Multicollinearity
Variable Selection Methods
An Application of Variable Selection Methods
Mallows C p Statistic
Variable Selection Criteria
Using the Principal Components as Predictors in Multiple Regression
Logistic Regression
A Simple Example of Logistic Regression
Maximum Likelihood Estimation
Interpreting Logistic Regression Output
Inference: Are the Predictors Significant?
Interpreting the Logistic Regression Model
Interpreting a Logistic Regression Model for a Dichotomous redictor
Interpreting a Logistic Regression Model for a Polychotomous redictor
Interpreing a Logistic Regression Model for a Continuous Predictor
The Assumption of Linearity
The Zero-Cell Problem
Multiple Logistic Regression
Introducing Higher Order terms to Handle Non-Linearity
Validating the Logistic Regression Model
WEKA: Hands-On Analysis Using Logistic Regression
Na ve Bayes and Bayesian Networks
The Bayesian Approach
The Maximum a Posteriori (MAP) Classification
The Posterior Odds Ratio
Balancing the Data
Na ve Bayes Classification
Numeric Predictors for Na ve Bayes Classification
WEKA: Hands-On Analysis Using Na ve Bayes
Bayesian Belief Networks
Using the Bayesian Network to Find Probabilities
WEKA: Hands-On Analysis Using Bayes Net
Genetic Algorithms
Introduction to Genetic Algorithms
The Basic Framework of a Genetic Algorithm
A Simple Example of Genetic Algorithms 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
Case Study: Modeling Response to Direct-Mail Marketing
The Cross-Industry Standard Process for Data Mining: CRISP-DM
Business Understanding Phase
Data Understanding and Data Preparation Phases
The Modeling Phase and the Evaluation Phase
Index