Managing Data Mining Technologies in Organizations(c) Techniques and Applications 

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• 1. Table of Contents

• 2. BackCover

• 3. Managing Data Mining Technologies in Organizations: Techniques and Applications

• 4. Foreword

• 5. Preface

• 6. Chapter 1: Bayesian Networks as a Decision Support Tool in Credit Scoring Domain

  Abstract

• 7. Introduction

  Introduction

• 8. Credit Scoring System

  Credit Scoring System

• 9. Introduction to Bayesian Networks

  Introduction to Bayesian Networks

• 10. The Scope of the Economic-Financial Analysis of an Enterprise

  The Scope of the Economic-Financial Analysis of an Enterprise

• 11. Building a Credit Scoring System

  Building a Credit Scoring System Building a Traditional Scoring System

• 12. Credit Scoring System-The Bayesian Network Implementation

  Credit Scoring SystemThe Bayesian Network Implementation

• 13. Conclusions

  Conclusions

• 14. References

  References

• 15. Endnotes

  Endnotes

• 16. Chapter 2: Frontier versus Ordinary Regression Models for Data Mining

  Abstract

• 17. Introduction

  Introduction

• 18. Pure Frontier Models

  Pure Frontier Models

• 19. Contrasts of Meaning and Purpose

  Contrasts of Meaning and Purpose

• 20. Data Mining Uses and Suggested Guidelines

  Data Mining Uses and Suggested Guidelines

• 21. Other Models and Applications

  Other Models and Applications

• 22. Conclusions

  Conclusions

• 23. References

  References

• 24. Chapter 3: An Evolutionary Misclassification Cost Minimization Approach for Medical Diagnosis

  Overview

• 25. Abstract

  Abstract

• 26. Introduction

  Introduction

• 27. Overview of Discriminant Analysis and Genetic Algorithm Approaches to Discriminant Analysis

  Overview of Discriminant Analysis and Genetic Algorithm Approaches to Discriminant Analysis

• 28. Integrating Type I and Type II Cost Preferences in Genetic-Algorithm-Based Classification Systems

  Integrating Type I and Type II Cost Preferences in Genetic-Algorithm-Based Classification Systems

• 29. Experiments on Simulated and Real-Life Data Sets

  Experiments on Simulated and Real-Life Data Sets Simulated Data Heart Disease Data BUPA Liver Disorders Data

• 30. Summary and Directions for Future Work

  Summary and Directions for Future Work

• 31. References

  References

• 32. Chapter 4: Guiding Knowledge Discovery Through Interactive Data Mining

  Abstract

• 33. Introduction

  Introduction

• 34. User Participation

  User Participation

• 35. Presentation

  Presentation

• 36. Presentation of Mining Results

  Presentation of Mining Results Clustering Association Presentations

• 37. Human Computer Interaction

  Human Computer Interaction Direct Manipulation Interactive Views Distortion

• 38. Interactive Data Mining

  Interactive Data Mining Interactive Clustering Interactive Association Mining

• 39. Conclusion

  Conclusion

• 40. References

  References

• 41. Chapter 5: A Proposed Process for Performing Data Mining Projects

  Abstract

• 42. Introduction

  Introduction

• 43. Literature Review

  Literature Review Data Warehousing Overview Data Mining Basics Evaluation of Alternative Processes

• 44. Research Methodology

  Research Methodology Data Collection Methods

• 45. Case Study Analysis and Discussion of Findings

  Case Study Analysis and Discussion of Findings

• 46. Conclusions

  Conclusions Implications for Practitioners and Managers Directions for Future Research

• 47. References

  References

• 48. Endnote

  Endnote

• 49. Chapter 6: Data Mining for Optimal Combination Demand Forecasts

  Overview

• 50. Abstract

  Abstract

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