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Data Mining Methods and Models

ISBN-10: 0471666564
ISBN-13: 9780471666561
Edition: 2006
Authors: Daniel T. Larose
List price: $139.00 Buy it from $19.38
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Description: This is an introduction to data mining methods and models, including association rules, clustering, K-nearest neighbour, statistical interference and much more. It presents a unified approach based on CRISP methodology.

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Book details

List price: $139.00
Copyright year: 2006
Publisher: John Wiley & Sons, Incorporated
Publication date: 1/30/2006
Binding: Hardcover
Pages: 344
Size: 6.25" wide x 9.50" long x 1.00" tall
Weight: 1.760
Language: English

This is an introduction to data mining methods and models, including association rules, clustering, K-nearest neighbour, statistical interference and much more. It presents a unified approach based on CRISP methodology.

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 Predictor
Interpreting a Logistic Regression Model for a Polychotomous Predictor
Interpreting 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

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