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

Managing Data Mining Technologies in Organizations: Techniques and Applications

ISBN: 1591400570
EAN: 2147483647

Year: 2003
Pages: 174

Authors: Parag Pendharkar

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Table of Contents
BackCover
Managing Data Mining Technologies in Organizations: Techniques and Applications
Foreword
Preface
Chapter 1: Bayesian Networks as a Decision Support Tool in Credit Scoring Domain
Introduction
Credit Scoring System
Introduction to Bayesian Networks
The Scope of the Economic-Financial Analysis of an Enterprise
Building a Credit Scoring System
Credit Scoring System-The Bayesian Network Implementation
Conclusions
References
Endnotes
Chapter 2: Frontier versus Ordinary Regression Models for Data Mining
Introduction
Pure Frontier Models
Contrasts of Meaning and Purpose
Data Mining Uses and Suggested Guidelines
Other Models and Applications
Conclusions
References
Chapter 3: An Evolutionary Misclassification Cost Minimization Approach for Medical Diagnosis
Abstract
Introduction
Overview of Discriminant Analysis and Genetic Algorithm Approaches to Discriminant Analysis
Integrating Type I and Type II Cost Preferences in Genetic-Algorithm-Based Classification Systems
Experiments on Simulated and Real-Life Data Sets
Summary and Directions for Future Work
References
Chapter 4: Guiding Knowledge Discovery Through Interactive Data Mining
Introduction
User Participation
Presentation
Presentation of Mining Results
Human Computer Interaction
Interactive Data Mining
Conclusion
References
Chapter 5: A Proposed Process for Performing Data Mining Projects
Introduction
Literature Review
Research Methodology
Case Study Analysis and Discussion of Findings
Conclusions
References
Endnote
Chapter 6: Data Mining for Optimal Combination Demand Forecasts
Abstract
Introduction
Methodology and Experiments
The Series
Individual Forecasting Methods
Alternative Weighting Methods
Performance Measurements
Results of Forecasting Experiments
Hybrid Weighting Method
Common Forecasting Methods for Every Item
Introducing Seasonality into the Forecasts
Conclusions
Reference
Chapter 7: The Myth of Enterprise Database Redesign
Abstract
Introduction
Theoretical Lens
Research Methodology
Format of Interviews
Emic Perspective
Discussion and Conclusions
References
Chapter 8: New Information Technologies and Other Pertinent Issues Impacting the Strategic Dimension of CRM for Business Excellence
Abstract
Introduction
History of CRM, CRM s Goal and the Role of Information Technology in CRM
Why CRM
Issues Influencing Success of CRM Solutions
Current Issues with CRM in Business
Examples of Applications of CRM in Business
Trends in CRM (Mainly the One Towards Customer-Centricity)
New Technologies in Support of CRM Including DWH
CRM as a Decision Support System
Examples of CRM Software Vendors and Applications in Business
Benefits of ECRM
CRM and Business Strategy
Future Areas of Research in CRM
Recommendations for CRM as a Strategic Tool for Business Excellence
Conclusion
References
Chapter 9: Utilization of Data Mining Techniques to Detect and Predict Accounting Fraud: A Comparison of Neural Networks and Discriminant Analysis
Introduction
Objectives
Data Mining
Future Research Considerations
Methodology
Results
Conclusions and Recommendations
Limitations
References
Chapter 10: A Multidimensional Data Warehouse Development Methodology
Abstract
Introduction
Multidimensional Conceptual Model
Methodology
Example
Summary and Conclusions
References
Chapter 11: A Telecommunications Model for Managing Complexity of Voice and Data Networks and Services
Introduction
Rationale
Convergence Capabilities
Service Complexity Improvements
Modern Technologies Used
Inter-Domain Architecture
Algorithms
Conclusions
Acknowledgments
References
Chapter 12: Combination Forecasts Based on Markov Chain Monte Carlo Estimation of the Mode
Abstract
Introduction
Model Development and Sufficiency Results
Individual and Combination Forecasts
Bayesian Approach
Markov Chain Monte Carlo Techniques
Log-Concavity Conditions
Simulation Studies
Empirical Illustration
Conclusions
References
Chapter 13: Web Mining: Creating Structure Out of Chaos
Introduction
Information Overload
Chaotic Structure of the Web
Web Mining
Hub and Authority Model
Hub and Spoke Model
Benefits of Hub and Spoke Model
Web Usage Mining
Conclusions
Reference
Chapter 14: Connectionist and Evolutionary Models for Learning, Discovering and Forecasting Software Effort
Introduction
Review of Literature on Data Mining
Introductory Concepts and Design Considerations for Non-Linear Connectionist and Evolutionary Models
Data Mining for Discovering Software Effort Patterns
Experiment and Results
Managerial Significance, Conclusions and Future Work
References
Index
Index_B
Index_C
Index_D
Index_E
Index_F
Index_G
Index_H
Index_I
Index_K
Index_L
Index_M
Index_N
Index_O
Index_P
Index_Q
Index_R
Index_S
Index_T
Index_U
Index_V
Index_W
List of Figures
List of Tables