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Data mining methods and models /

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.

Bibliographic Details
Main Author: Larose, Daniel T., (Author)
Format: eBook
Language:English
Published: Hoboken, NJ : Wiley-Interscience, [2006]
Subjects:
Data mining.
Exploration de données (Informatique)
COMPUTERS > Desktop Applications > Databases.
COMPUTERS > Database Management > General.
COMPUTERS > System Administration > Storage & Retrieval.
Electrical and Electronics Engineering
Electronic books.
Online Access:View fulltext via EzAccess
Table of Contents:
  • 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.
  • Naïve 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.

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