Stéphane Tufféry

Data Mining and Statistics for Decision Making

• Gebundenes Buch

Produktbeschreibung

This practical guide to understanding and implementing data mining techniques discusses traditional methods--cluster analysis, factor analysis, linear regression, PLS regression, and generalized linear models--and recent methods--bagging and boosting, decision trees, neural networks, support vector machines, and genetic algorithm. The book focuses largely on credit scoring, one of the most common applications of predictive techniques, but also includes other descriptive techniques, such as customer segmentation. It also covers data mining with R, provides a comparison of SAS and SPSS, and includes an appendix presenting the necessary statistical background.

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Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: * Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. * Starts from basic principles up to advanced concepts. * Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. * Gives practical tips for data mining implementation to solve real world problems. * Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. * Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

Produktdetails

• Verlag: John Wiley & Sons / Wiley
• Artikelnr. des Verlages: 14568829000
• Seitenzahl: 720
• Erscheinungstermin: 18. April 2011
• Englisch
• Abmessung: 250mm x 175mm x 43mm
• Gewicht: 1395g
• ISBN-13: 9780470688298
• ISBN-10: 0470688297
• Artikelnr.: 32567635

Autorenporträt

Dr Stephane Tuffery teaches Data Mining and statistics, University Rennes 1, Paris, France. Translator, Rod Riesco, UK.

Inhaltsangabe

Preface. Foreword. Foreword from the French language edition. List of
trademarks. 1. Oveview of data mining. 1.1 What is data mining? 1.2 What is
data mining used for? 1.3 Data mining and statistics. 1.4 Data mining and
information technology. 1.5 Data mining and protection of persona; data.
1.6 Implementation of data mining. 2. The development of a data mining
study. 2.1 Defining the aims. 2.2 Listing the existing data. 2.3 Collecting
the data. 2.4 Exploring and preparing the data. 2.5 Population
segmentation. 2.6 Drawing up and validating predictive models. 2.7
Synthesizing predictive models of different segments. 2.8 Iteration of the
preceding steps. 2.9 Deploying the models. 2.10 Training the model users.
2.11 Monitoring the models. 2.12 Enriching the models. 2.13 Remarks. 2.14
Life cycle of a model. 2.15 Costs of a pilot project. 3. Data Exploration
and preparation. 3.1 The different types of data. 3.2 Examining the
distribution of variables. 3.3 Detection of rare or missing values. 3.4
Detection of aberrant values. 3.5 Detection of extreme values. 3.6 Tests of
normality. 3.7 Homoscedasticity and heteroscedasticity. 3.8 Detection of
the most discriminating variables. 3.9 Transformation of variables. 3.10
Choosing ranges of values of binned variables. 3.11 Creating new variables.
3.12 Detecting interactions. 3.13 Automatic variable selection. 3.14
Detection of collinearity. 3.15 Sampling. 4. Using commercial data. 4.1
Data used in commercial applications. 4.2 Special data. 4.3 Data used by
business sector. 5. Statistical and data mining software. 5.1 Types of data
mining and statistical software. 5.2 Essential characteristics of the
software. 5.3 The main software packages. 5.4 Comparison of R, SAS and IBM
SPSS. 5.5 How to reduce processing time. 6. An outline of data mining
methods. 6.1 Classification of the methods. 6.2 Comparison of the methods.
7. Factor analysis. 7.1 Principal component analysis. 7.2 Variants of
principal component analysis. 7.3 Correspondence analysis. 7.4 Multiple
correspondence analysis. 8. Neural networks. 8.1 General information on
neural networks. 8.2 Structure of a neural network. 8.3 Choosing the
learning sample. 8.4 Some empirical rules for network design. 8.5 Data
normalization. 8.6 Learning algorithms. 8.7 The main neural networks. 9.
Cluster analysis. 9.1 Definition of clustering. 9.2 Applications of
clustering. 9.3 Complexity of clustering. 9.4 Clustering structures. 9.5
Some methodological considerations. 9.6 Comparison of factor analysis and
clustering. 9.7 Within-cluster and between-cluster sum of squares. 9.8
Measurements of clustering quality. 9.9 Partitioning methods. 9.10
Agglomerative hierarchical clustering. 9.11 Hybrid clustering methods. 9.12
Neural clustering. 9.13 Clustering by similarity aggregation. 9.14
Clustering of numeric variables. 9.15 Overview of clustering methods. 10.
Association analysis. 10.1 Principles. 10.2 Using taxonomy. 10.3 Using
supplementary variables. 10.4 Applications. 10.5 Example of use. 11.
Classification and prediction methods. 11.1 Introduction. 11.2 Inductive
and transductive methods. 11.3 Overview of classification and prediction
methods. 11.4 Classification by decision tree. 11.5 Prediction by decision
tree. 11.6 Classification by discriminant analysis. 11.7 Prediction by
linear regression. 11.8 Classification by logistic regression. 11.9
Developments in logistic regression. 11.10 Bayesian methods. 11.11
Classification and prediction by neural networks. 11.12 Classification by
support vector machines. 11.13 Prediction by genetic algorithms. 11.14
Improving the performance of a predictive model. 11.15 Bootstrapping and
ensemble methods. 11.16 Using classification and prediction methods. 12. An
application of data mining: scoring. 12.1 The different types of score.
12.2 Using propensity scores and risk scores. 12.3 Methodology. 12.4
Implementing a strategic score. 12.5 Implementing an operational score.
12.6 Scoring solutions used in a business. 12.7 An example of credit
scoring (data preparation). 12.8 An example of credit scoring (modeling by
logistic regression). 12.9 An example of credit scoring (modeling by
DISQUAL discriminant analysis). 12.10 A brief history of credit scoring.
13. Factors for success in a data mining project. 13.1 The subject. 13.2
The people. 13.3 The data. 13.4 The IT systems. 13.5 The business culture.
13.6 Data mining: eight common misconceptions. 13.7 Return on investment.
14. Text mining. 14.1 Definition of text mining. 14.2 Text sources used.
14.3 Using text mining. 14.4 Information retrieval. 14.5 Information
extraction. 14.6 Multi-type data mining. 15. Web mining. 15.1 The aims of
web mining. 15.2 Global analyses. 15.3 Individual analyses. 15.4 Personal
analysis. Appendix A. Elements of statistics. Appendix B. Further reading.
Index.