Oxford.Data Analysis and Data Mining.2012 Using R.pdf

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Oxford.Data Analysis and Data Mining.2012 Using R.pdf (3.59 MB, 需要: 10 个论坛币)

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Oxford.Data Analysis and Data Mining.2012 Using R.pdf
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This work, now translated into English, is the updated version of the first edition,
which appeared in Italian (Azzalini & Scarpa 2004).
The new material is of two types. First, we present some new concepts and
methods aimed at improving the coverage of the field, without attempting to be
exhaustive in an area that is becoming increasingly vast. Second, we add more case
studies. The work maintains its character as a first course in data analysis, and we
assume standard knowledge of statistics at graduate level.
Complementary materials (data sets, R scripts) are available at: http://
azzalini.stat.unipd.it/Book-DM/.

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关键词：Data Mining Analysis Analysi Oxford Analys character becoming concepts material English

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Preface
Preface to the English Edition
Introduction
New problems and new opportunities
Data, more data, and data mines
Problems in mining
SQL, OLTP, OLAP, DWH and KDD
Complications
All models are wrong
What is a model?
From data to model
A matter of style
Press the button?
Tools for computation and graphics
A-B-C
Old friends: Linear models
Basic concepts
Variable transformations
Multivariate responses
Computational aspects
Computational aspects
Least squares estimation by successive orthogonalization
When n is large
Recursive estimation
Likelihood
General concepts
Linear models with Gaussian error terms
Binary variables with binomial distribution
Logistic regression and GLM
Exercises
Optimism, Conflicts, and Trade-offs
Matching the conceptual frame and real life
A simple prototype problem
If we knew f(x)...
But as we do not know f(x)...
Methods for model selection
Training sets and test sets
Cross-validation
Criteria based on information
Reduction of dimensions and selection of most appropriate model
Automatic selection of variables
Principal component analysis
Methods of regularization
Exercises
Prediction of Quantitative Variables
Nonparametric estimation: Why?
Local regression
Basic formulation
Choice of smoothing parameters
Variability bands
Variable bandwidths and loess
Extension to several dimensions
The curse of dimensionality
Splines
Spline functions
Regression splines
Smoothing splines
Multidimensional splines
MARS
Additive models and GAM
Projection pursuit
Inferential aspects
Effective degrees of freedom
Analysis of variance
Regression trees
Approximations via step functions
Regression trees: growth
Regression trees: pruning
Discussion
Neural networks
Case studies
Traffic prediction in telecommunications
Insurance pricing
Exercises
Methods of Classification
Prediction of categorical variables
An introduction based on a marketing problem
Prediction via logistic regression
Misclassification tables and adequacy measures
ROC curve
Lift curve
Extension to several categories
Multivariate logit and multinomial regression
Ordinal categorical variables and cumulative logit models
Classification via linear regression
Case with two categories
Case with several categories
Discussion
Discriminant analysis
General remarks
Linear discriminant analysis
Quadratic discriminant analysis
Discussion
Some nonparametric methods
Classification trees
Some other topics
Neural networks
Support vector machines
Combination of classifiers
Bagging
Boosting
Random forests
Case studies
The traffic of a telephone company
Churn analysis
Customer satisfaction
Web usage mining
Exercises
Methods of Internal Analysis
Cluster analysis
General remarks
Distances and dissimilarities
Non-hierarchical methods
Hierarchical methods
Associations among variables
Elementary notions of graphical models
Association rules
Case study: Web usage mining
Profiling website visitors
Sequence rules and usage behaviour
Appendix A Complements of Mathematics and Statistics
Concepts on linear algebra
Concepts of probability theory
Concepts of linear models
Appendix B Data Sets
Simulated data
Car data
Brazilian bank data
Data for telephone company customers
Insurance data
Choice of fruit juice data
Customer satisfaction
Web usage data
Appendix C Symbols and Acronyms
References
Author Index
Subject Index

thanks for sharing.

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thanks for sharing