Applied Data Mining for Business and Industry

ISBN-10: 0470058870

ISBN-13: 9780470058879

Edition: 2nd 2009

List price: $81.00
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Description: This new edition sees the inclusion of 70ew material, including eight new case studies, that brings this best selling title up to date with the many advances made in the field since its original publication. In the text all the methods described are either computational or of a statistical modelling nature; complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of both students and industry professionals. A number of case studies are explored, taken from Giudici's own applied work in industry, that demonstrate how the methods described can be applied to real problems. The book is supported by website featuring data sets, software and additional material.

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Book details

List price: $81.00
Edition: 2nd
Copyright year: 2009
Publisher: John Wiley & Sons, Incorporated
Publication date: 5/26/2009
Binding: Paperback
Pages: 258
Size: 6.00" wide x 9.00" long x 0.75" tall
Weight: 0.880
Language: English

Introduction
Methodology
Organisation of the data
Statistical units and statistical variables
Data matrices and their transformations
Complex data structures
Summary
Summary statistics
Univariate exploratory analysis
Measures of location
Measures of variability
Measures of heterogeneity
Measures of concentration
Measures of asymmetry
Measures of kurtosis
Bivariate exploratory analysis of quantitative data
Multivariate exploratory analysis of quantitative data
Multivariate exploratory analysis of qualitative data
Independence and association
Distance measures
Dependency measures
Model-based measures
Reduction of dimensionality
Interpretation of the principal components
Further reading
Model specification
Measures of distance
Euclidean distance
Similarity measures
Multidimensional scaling
Cluster analysis
Hierarchical methods
Evaluation of hierarchical methods
Non-hierarchical methods
Linear regression
Bivariate linear regression
Properties of the residuals
Goodness of fit
Multiple linear regression
Logistic regression
Interpretation of logistic regression
Discriminant analysis
Tree models
Division criteria
Pruning
Neural networks
Architecture of a neural network
The multilayer perceptron
Kohonen networks
Nearest-neighbour models
Local models
Association rules
Retrieval by content
Uncertainty measures and inference
Probability
Statistical models
Statistical inference
Non-parametric modelling
The normal linear model
Main inferential results
Generalised linear models
The exponential family
Definition of generalised linear models
The logistic regression model
Log-linear models
Construction of a log-linear model
Interpretation of a log-linear model
Graphical log-linear models
Log-linear model comparison
Graphical models
Symmetric graphical models
Recursive graphical models
Graphical models and neural networks
Survival analysis models
Further reading
Model evaluation
Criteria based on statistical tests
Distance between statistical models
Discrepancy of a statistical model
Kullback-Leibler discrepancy
Criteria based on scoring functions
Bayesian criteria
Computational criteria
Criteria based on loss functions
Further reading
Business case studies
Describing website visitors
Objectives of the analysis
Description of the data
Exploratory analysis
Model building
Cluster analysis
Kohonen networks
Model comparison
Summary report
Market basket analysis
Objectives of the analysis
Description of the data
Exploratory data analysis
Model building
Log-linear models
Association rules
Model comparison
Summary report
Describing customer satisfaction
Objectives of the analysis
Description of the data
Exploratory data analysis
Model building
Summary
Predicting credit risk of small businesses
Objectives of the analysis
Description of the data
Exploratory data analysis
Model building
Model comparison
Summary report
Predicting e-learning student performance
Objectives of the analysis
Description of the data
Exploratory data analysis
Model specification
Model comparison
Summary report
Predicting customer lifetime value
Objectives of the analysis
Description of the data
Exploratory data analysis
Model specification
Model comparison
Summary report
Operational risk management
Context and objectives of the analysis
Exploratory data analysis
Model building
Model comparison
Summary conclusions
References
Index
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