Contents

• Introduction
• The Self-Organizing Map
  • Structure
  • Initilialization
  • Training
  • Mathematical treatment
    • Voronoi region:
    • Convergence:
    • Energy function:
  • Important properties
    • Quantization and projection:
    • Errors in data:
  • Visualization
    • Vector components:
    • Clusters:
    • SOM in the input space:
    • Input vectors on the SOM:
• Knowledge Discovery With the SOM
  • Visualization
    • Map visualization:
    • Object visualization:
  • Map measures
    • Topology
    • Resolution
    • Combined quality measures
    • Similarity between maps
  • Clustering
    • Clustering the SOM
      • Distance matrix
      • Hierarchical SOMs
      • Other clustering methods
    • Classification
    • Probability density estimation
  • Modeling
    • Lookup models
    • Local models
• A Data Mining Tool
  • Data sets
    • Component scaling:
    • Histogram equalization:
    • Filtering:
    • Grasping the meaning:
  • Map training
  • Map visualization
  • Data on map
  • Postprocessing
  • Futher development
• Pulp and Paper Industry
  • Pulp and paper technology
    • Paper machines and pulp lines
    • Mill techonology
    • Geographical areas
  • Scandinavian paper industry
    • Data sets
    • Environmental, economical and technological aspects
    • Combined maps
• Conclusions
• References
• About this document ...