A Practical Guide to Data Mining for Business and Industry by Ahlemeyer-Stubbe, Andrea -- Read -- Imperial Library of Trantor

Index

Cover Title page Copyright page Glossary of terms Part I: Data mining concept

1 Introduction

1.1 Aims of the Book 1.2 Data Mining Context 1.3 Global Appeal 1.4 Example Datasets Used in This Book 1.5 Recipe Structure 1.6 Further Reading and Resources

2 Data mining definition

Part II: Data mining Practicalities

3 All about data

3.1 Some Basics 3.2 Data Partition: Random Samples for Training, Testing and Validation 3.3 Types of Business Information Systems 3.4 Data Warehouses 3.5 Three Components of a Data Warehouse: DBMS, DB and DBCS 3.6 Data Marts 3.7 A Typical Example from the Online Marketing Area 3.8 Unique Data Marts 3.9 Data Mart: Do’s and Don’ts

4 Data Preparation

4.1 Necessity of Data Preparation 4.2 From Small and Long to Short and Wide 4.3 Transformation of Variables 4.4 Missing Data and Imputation Strategies 4.5 Outliers 4.6 Dealing with the Vagaries of Data 4.7 Adjusting the Data Distributions 4.8 Binning 4.9 Timing Considerations 4.10 Operational Issues

5 Analytics

5.1 Introduction 5.2 Basis of Statistical Tests 5.3 Sampling 5.4 Basic Statistics for Pre-analytics 5.5 Feature Selection/Reduction of Variables 5.6 Time Series Analysis

6 Methods

6.1 Methods Overview 6.2 Supervised Learning 6.3 Multiple Linear Regression for Use When Target is Continuous 6.4 Regression When the Target is Not Continuous 6.5 Decision Trees 6.6 Neural Networks 6.7 Which Method Produces the Best Model? A Comparison of Regression, Decision Trees and Neural Networks 6.8 Unsupervised Learning 6.9 Cluster Analysis 6.10 Kohonen Networks and Self-Organising Maps 6.11 Group Purchase Methods: Association and Sequence Analysis

7 Validation and Application

7.1 Introduction to Methods for Validation 7.2 Lift and Gain Charts 7.3 Model Stability 7.4 Sensitivity Analysis 7.5 Threshold Analytics and Confusion Matrix 7.6 ROC Curves 7.7 Cross-Validation and Robustness 7.8 Model Complexity

Part III: Data mining in action

8 Marketing

9 Intra-Customer Analysis

10 Learning from a Small Testing Sample and Prediction

11 Miscellaneous

12 Software and Tools

12.1 List of Requirements When Choosing a Data Mining Tool 12.2 Introduction to the Idea of Fully Automated Modelling (FAM) 12.3 FAM Function 12.4 FAM Architecture 12.5 FAM Data Flows and Databases 12.6 FAM Modelling Aspects 12.7 FAM Challenges and Critical Success Factors 12.8 FAM Summary

13 Overviews

13.1 To Make Use of Official Statistics 13.2 How to Use Simple Maths to Make an Impression 13.3 Differences between Statistical Analysis and Data Mining 13.4 How to Use Data Mining in Different Industries 13.5 Future Views

Bibliography Index End User License Agreement