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Explanatory Variables in Classifications and the Detection of the Optimum Number of Clusters

  • János Podani
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

An ordinal approach to the a posteriori evaluation of the explanatory power of variables in classifications is proposed. The contribution of each variable is assessed in a way fully compatible with the distance or dissimilarity function used in the clustering process. Then, a simple ranking-based measure is applied to express the relative agreement or disagreement of variables with a given partition. This measure treats all variables equally, no matter how influential they were when the classification was actually created. The sum of measures for all variables reflects their overall agreement and can be used to select an optimal partition from a hierarchical classification.

Keywords

Explanatory Power Rank Order Hierarchical Classification Vegetational Plot Relative Agreement 
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

  • János Podani
    • 1
  1. 1.Department of Plant Taxonomy and EcologyLoránd Eötvös UniversityBudapestHungary

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