Data Mining and Statistics for Decision Making E-Book

Stéphane Tufféry

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1164

Veröffentlichungsjahr: 2011

Beschreibung

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.

Leseprobe

Table of Contents

Cover

Title

Copyright

Dedication

Preface

Foreword

Foreword from the French language edition

List of trademarks

Chapter 1: Overview 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 Personal Data

1.6 Implementation of Data Mining

Chapter 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

Chapter 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

Chapter 4: Using commercial data

4.1 Data used in Commercial Applications

4.2 Special Data

4.3 Data Used by Business Sector

Chapter 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

Chapter 6: An outline of data mining methods

6.1 Classification of the Methods

6.2 Comparison of the Methods

Chapter 7: Factor analysis

7.1 Principal Component Analysis

7.2 Variants of Principal Component Analysis

7.3 Correspondence Analysis

7.4 Multiple Correspondence Analysis

Chapter 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

Chapter 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

Chapter 10: Association analysis

10.1 Principles

10.2 Using Taxonomy

10.3 Using Supplementary Variables

10.4 Applications

10.5 Example of Use

Chapter 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

Chapter 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 (Modelling by Logistic Regression)

12.9 An Example of Credit Scoring (Modelling by DISQUAL discriminant analysis)

12.10 A Brief History of Credit Scoring

References

Chapter 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

Chapter 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

Chapter 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

A.1 A Brief History

A.2 Elements of Statistics

A.3 Statistical Tables

Appendix B: Further reading

B.1. Statistics and Data Analysis

B.2. Data Mining and Statistical Learning

B.3. Text Mining

B.4. Web Mining

B.5. R Software

B.6. SAS Software

B.7. IBM SPSS Software

B.8. Websites

Index

End User License Agreement

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Guide

Cover

Table of Contents

Begin Reading

List of Illustrations

Chapter 1: Overview of data mining

Figure 1.1 The customer relationship circuit.

Figure 1.2 IT architecture for data mining.

Figure 1.3 Example of a decision tree generated by Answer Tree.

Figure 1.4 Example of SPSS code for a decision tree.

Figure 1.5 Example of SAS code generated by SAS Enterprise Miner.

Figure 1.6 Exporting a model into PMML in R software

Chapter 2: The development of a data mining study

Figure 2.1 Developing a predictive analysis base.

Figure 2.2 ROC curve.

Figure 2.3 Costs of a data mining project

Chapter 3: Data exploration and preparation