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Strata Decision Tree SDA Software

  • M. Carmen Bravo
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The SDT and SDTEDITOR software are presented. The SDT (Strata Decision Tree) implements a generalised recursive tree-building algorithm for populations partitioned into strata and described by symbolic data, that is, more complex data structures than classical data. Symbolic objects describe decisional nodes and strata. The SDTEDITOR is a graph editor for strata decision trees. The SDT and SDTEDITOR are modules integrated into the SODAS Software (Symbolic Official Data Analysis System), partially supported by ESPRIT-20821 SODAS.

Keywords

Terminal Node Symbolic Data Classical Data Decisional Node Graph Editor 
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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References

  1. BRAVO, M.C.(1999): Software User Manual for the Strata Decision Tree CSCI vO4. SODAS Project (20821 DG34/D-3/300536). Comm. of the EC-DgIII-Eurostat.Google Scholar
  2. BRAVO,M.C. and GARCiA-SANTESMASES,J.M.(1997): Segmentation Trees for Stratified Data. In: Jansen,J. and Lauro,C.N.Ed: Applied Stochastic Models and Data Analysis: The Ins/Outs of Solving Real Problems. Curto, Napoli, 37–42.Google Scholar
  3. BRAVO,M.C. and GARCiA-SANTESMASES,J.M.(2000a): Segmentation Trees for Stratified Data. In: Bock,H.H. and Di-day,E. Eds.: Analysis of Symbolic Data. Exploratory Methods for Extracting Statistical Information from Complex Data. Springer Verlag, Heid., 266–293.Google Scholar
  4. BRAVO,M.C. and GARCiA-SANTESMASES,J.M.(2000b), Symbolic Object Description of Strata by Segmentation Trees. In: Computational Statistics. Physica Verlag, Heidelberg, 12p. To appearGoogle Scholar
  5. BREIMAN,L., FRIEDMAN,J.H., OLSHEN,R.A., STONE,C.J. Classification and Regression Trees. Wadsworth, Beimond, Ca.Google Scholar
  6. CIAMPI,A., DID AY, E., LEBBE,J., PÉRINEL,E. and VIGNES, R.(1996): Recursive partition with probabilistically imprecise data. In: Diday,E. et al. Eds.: Ordinal and symbolic data analysis. Springer Verlag. 201–212.CrossRefGoogle Scholar
  7. DIDAY,E.(1993): An introduction to Symbolic Data Analysis. Tutorial of the 4th conference of IFCS, Rapport INRIA no. 1936, Paris.Google Scholar
  8. MORINEAU, A.(2000): The SODAS Software Package. In: Bock,H.H. and Diday,E. Eds.: Analysis of Symbolic Data. Exploratory Methods for Extracting Statistical Information from Complex Data. Springer Verlag, Heid., 386–391.Google Scholar
  9. QUINLAN,J.R.(1990), Probabilistic Decision Trees, In: Kodratoff.Y., Michalski,R. Ed. Machine Learning, an Artificial Intelligence A., III. Kaufmann, 140–152.Google Scholar
  10. STÉPHAN, V., HÉBRAIL, G. and LECHEVALIER, Y. (2000): Generation of Symbolic Objects from Relational Data Bases. In: Bock,H.H. and Diday.E. Eds.: Analysis of Symbolic Data. Exploratory Methods for Extracting Statistical Information from Complex Data. Springer Verlag, Heidelberg, 78–105.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 2000

Authors and Affiliations

  • M. Carmen Bravo
    • 1
  1. 1.Universidad Complutense de Madrid, Centro de Proceso de DatosMadridSpain

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