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Fitting Graphs and Trees with Multidimensional Scaling Methods

  • Willem J. Heiser
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

The symmetric difference between sets of qualitative elements (called features) forms the basis of a distance model that can be used as a general framework for fitting a particular class of graphs, which includes additive trees, hierarchical trees and circumplex structures. It is shown how to parametrize this fitting problem in terms of a lattice of subsets, and how inclusion relations between feature sets lead to additivity of distance along paths in a graph. An algorithm based on alternating least squares and on the recent method of cluster differences scaling is described, and illustrated for the general case.

Keywords

Loss Function Edge Length Latent Node Feature Distance Feature Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Japan 1998

Authors and Affiliations

  • Willem J. Heiser
    • 1
  1. 1.Department of Data TheoryLeiden UniversityLeidenThe Netherlands

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