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Interactive Decision Tree Construction for Interval and Taxonomical Data

  • François Poulet
  • Thanh-Nghi Do
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4404)

Abstract

Visual data-mining strategy lies in tightly coupling the visualizations and analytical processes into one data-mining tool that takes advantage of the assets from multiple sources. This paper presents two graphical interactive decision tree construction algorithms able to deal either with (usual) continuous data or with interval and taxonomical data. They are the extensions of two existing algorithms: CIAD [17] and PBC [3]. Both CIAD and PBC algorithms can be used in an interactive or cooperative mode (with an automatic algorithm to find the best split of the current tree node). We have modified the corresponding help mechanisms to allow them to deal with interval-valued attributes. Some of the results obtained on interval-valued and taxonomical data sets are presented with the methods we have used to create these data sets.

Keywords

Interval Data Support Vector Machine Algorithm Decision Tree Algorithm Good Split Taxonomical Data 
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-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • François Poulet
    • 1
  • Thanh-Nghi Do
    • 2
  1. 1.IRISA-TexmexUniversité de Rennes IRennes CedexFrance
  2. 2.Equipe InSitu INRIA Futurs, LRI, Bat.490Université Paris SudOrsay CedexFrance

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