draw.decisionTree.SDA: Draws optimal split based decision tree for symbolic objects

View source: R/decisionTree.SDA.r

draw.decisionTree.SDAR Documentation

Draws optimal split based decision tree for symbolic objects

Description

Draws optimal split based decision tree for symbolic objects

Usage

draw.decisionTree.SDA(decisionTree.SDA,boxWidth=1,boxHeight=3)

Arguments

decisionTree.SDA

optimal split based decision tree for symbolic objects (result of decisionTree.SDA function)

boxWidth

witdh of single box in drawing

boxHeight

height of single box in drawing

Details

Draws optimal split based decision (classification) tree for symbolic objects.

Value

A draw of optimal split based decision (classification) tree for symbolic objects.

Author(s)

Andrzej Dudek andrzej.dudek@ue.wroc.pl Marcin Pełka marcin.pelka@ue.wroc.pl

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/symbolicDA/

References

Billard L., Diday E. (eds.) (2006), Symbolic Data Analysis, Conceptual Statistics and Data Mining, John Wiley & Sons, Chichester.

Bock H.H., Diday E. (eds.) (2000), Analysis of symbolic data. Explanatory methods for extracting statistical information from complex data, Springer-Verlag, Berlin.

Diday E., Noirhomme-Fraiture M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester.

See Also

decisionTree.SDA

Examples

# LONG RUNNING - UNCOMMENT TO RUN
# Files samochody.xml and wave.xml needed in this example 
# can be found in /inst/xml library of package

# Example 1
#sda<-parse.SO("samochody")
#tree<-decisionTree.SDA(sda, "Typ_samochodu~.", testSet=26:33)
#draw.decisionTree.SDA(tree,boxWidth=1,boxHeight=3)

# Example 2
#sda<-parse.SO("wave")
#tree<-decisionTree.SDA(sda, "WaveForm~.", testSet=1:30)
#draw.decisionTree.SDA(tree,boxWidth=2,boxHeight=3)

symbolicDA documentation built on May 28, 2022, 1:08 a.m.