• 1 Introduction
  • 2 Data mining
  • 2.1 The concept of data mining
  • 2.2 The general process of data mining
  • 2.3 The application architecture of data mining
  • 3 Classification decisions
  • 3.1 Two ways of computer-based classification decisions
  • 3.2 The inductive classification
  • 3.3 Various groups of classification techniques
  • 4 Various classification algorithms for credit scoring
  • 4.1 Discriminant Analysis: Bayesian Linear Discriminant Analysis
  • 4.2 Logistic Regression
  • 4.3 Instance-based learning
  • 4.4 Model Trees: M5
  • 4.5 Neural Networks: Multi-layer perceptron
  • 4.6 Comparisons of the introduced algorithms
  • 5 A framework of the data mining application process for credit scoring
  • 5.1 Reasons for a process framework
  • 5.2 The presentation of the general framework
  • 6 Summary and conclusion