symbolicDA (version 0.7-1)

interscal.SDA: Multidimensional scaling for symbolic interval data - InterScal algorithm

Description

Multidimensional scaling for symbolic interval data - InterScal algorithm

Usage

interscal.SDA(x,d=2,calculateDist=FALSE)

Value

xprim

coordinates of rectangles

stress.sym

final STRESSSym value

Arguments

x

symbolic interval data: a 3-dimensional table, first dimension represents object number, second dimension - variable number, and third dimension contains lower- and upper-bounds of intervals (Simple form of symbolic data table)

d

Dimensionality of reduced space

calculateDist

if TRUE x are treated as raw data and min-max dist matrix is calulated. See details

Author

Andrzej Dudek Marcin Pełka

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

Details

Interscal is the adaptation of well-known classical multidimensional scaling for symbolic data. The input for Interscal is the interval-valued dissmilirarity matrix. Such dissmilarity matrix can be obtained from symbolic data matrix (that contains only interval-valued variables), judgements obtained from experts, respondents. See Lechevallier Y. (2001) for details on calculating interval-valued distance. See file ../doc/Symbolic_MDS.pdf for further details

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.

Lechevallier Y. (ed.), Scientific report for unsupervised classification, validation and cluster analysis, Analysis System of Symbolic Official Data - Project Number IST-2000-25161, project report.

See Also

iscal.SDA,symscal.SDA

Examples

# LONG RUNNING - UNCOMMENT TO RUN
#sda<-parse.SO("samochody")
#data<-sda$indivIC
#mds<-interscal.SDA(data, d=2, calculateDist=TRUE)