Giudici Paolo. Applied Data Mining, Statistical Methods for Business and Industry

Giudici, Paolo - Applied Data Mining, Statistical Methods for Business and Industry, John Wiley & Sons Ltd, -373 p.
Statistical data mining
Uncertainty measures and inference
Probability
Statistical models
Statistical inference
Non-parametric modeling
The normal linear model
Main inferential results
Application
Generalised linear models
The exponential family
Definition of generalised linear models
The logistic regression model
Application
Log-linear models
Construction of a log-linear model
Interpretation of a log-linear model
Graphical log-linear models
Log-linear model comparison
Application
Graphical models
Symmetric graphical models
Recursive graphical models
Graphical models versus neural networks
Further reading
Evaluation of data mining methods
Criteria based on statistical tests
Distance between statistical models
Discrepancy of a statistical model
The Kullback–Leibler discrepancy
Criteria based on scoring functions
Bayesian criteria
Computational criteria
Criteria based on loss functions
Further reading
Part II Business cases
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
Web clickstream analysis
Objectives of the analysis
Description of the data
Exploratory data analysis
Model building
Sequence rules
Link analysis
Probabilistic expert systems
Markov chains
Model comparison
Summary report
Profiling website visitors
Objectives of the analysis
Description of the data
Exploratory analysis
Model building
Cluster analysis
Kohonen maps
Model comparison
Summary report
Customer relationship management
Objectives of the analysis
Description of the data
Exploratory data analysis
Model building
Logistic regression models
Radial basis function networks
Classification tree models
Nearest-neighbour models
Model comparison
Summary report
Credit scoring
Objectives of the analysis
Description of the data
Exploratory data analysis
Model building
Logistic regression models
Classification tree models
Multilayer perceptron models
Model comparison
Summary report
Forecasting television audience
Objectives of the analysis
Description of the data
Exploratory data analysis
Model building
Model comparison
Summary report