文摘
英文文摘
DEDICATION
ACKNOWLEDGEMENTS
List of Figures
List of Tables
Chapter 1 Introduction
1.1 Brief Overview of Decision Support Systems
1.2 The necessity of Data Mining and the evolution of data collection systems
1.3 Definition of Data Mining
1.3.1 The objectives of Data Mining
1.4 Different Data Mining Functionalities
1.4.1 Class Description: Characterization and Discrimination
1.4.2 Mining Frequent Patterns, Associations, and Correlations
1.5 Data Mining Requirements
1.6 Motivation and Contribution of the Thesis
1.7 Research Questions
1.8 Outline of the Thesis
Chapter 2 An Overview of Data Mining Techniques
2.1 Introduction
2.2 Classical Techniques: Statistics, Neighborhoods and Clustering
2.2.1 Statistics
2.2.2 Nearest Neighbor
2.2.3 Classification
2.2.4 Clustering
2.2.5 Clustering and nearest neighbor association for prediction
2.2.6 Non-Hierarchical Clustering
2.2.7 Hierarchical Clustering
2.3 Next Generation Techniques: Trees, Networks and Rules
2.3.1 Decision Trees
2.3.2 Using decision trees for Exploration
2.3.3 Decision trees for Prediction
2.4 Neural Networks
2.4.1 Applying Neural Networks to Business
2.4.2 Neural Networks for prediction
Chapter3 Supplier Relationship Management
3.1 Definition
3.2 Customer Relationship Management
3.3 Supplier Relationship Management
3.3.1 SRM Drives competitive advantage
3.4 Improved Business Process Support
3.4.1 Improving specific procurement processes
3.4.2 Abstracting the ERP system
3.5 Next-Generation Architecture
3.5.1 Speeding Time to market
Chapter4 Integration of data mining and SRM
4.1 Objectives
4.1.1 Necessity of integrating DM to SRM
4.1.2 Possible tools to be used
4.1.3 Classification
4.1.4 Scoring
4.1.5 Preliminaries
4.2 Simulation
4.2.1 Predicting the winning bid (Supplier) of an order
4.2.2 Pre-processing
4.2.3 The IF-THEN rule of our classification
4.3 Performance of the Proposed Strategy
4.3.1 The Algorithm
4.3.2 Accuracy and Coverage
Chapter5 CONCLUSIONS
Summary
Eventual further work
REFERENCES
