A practical guide to data mining for business and industry / Andrea Ahlemeyer-Stubbe, Shirley Coleman.

Includes bibliographical references and index.

Print version record and CIP data provided by publisher.

"Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest."-- Unedited summary from book.

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; 2.1 Types of Data Mining Questions; 2.2 Data Mining Process; 2.3 Business Task: Clarification of the Business Question behind the Problem; 2.4 Data: Provision and Processing of the Required Data; 2.5 Modelling: Analysis of the Data; 2.6 Evaluation and Validation during the Analysis Stage

2.7 Application of Data Mining Results and Learning from the ExperiencePart 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 Strategies4.5 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 Networks6.7 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

8.1 Recipe 1: Response Optimisation: To Find and Address the Right Number of Customers8.2 Recipe 2: To Find the x% of Customers with the Highest Affinity to an Offer; 8.3 Recipe 3: To Find the Right Number of Customers to Ignore; 8.4 Recipe 4: To Find the x% of Customers with the Lowest Affinity to an Offer; 8.5 Recipe 5: To Find the x% of Customers with the Highest Affinity to Buy; 8.6 Recipe 6: To Find the x% of Customers with the Lowest Affinity to Buy; 8.7 Recipe 7: To Find the x% of Customers with the Highest Affinity to a Single Purchase