replication.SDA: Modification of replication analysis for cluster validation...

In symbolicDA: Analysis of Symbolic Data

Modification of replication analysis for cluster validation of symbolic data

Description

Replication analysis for cluster validation of symbolic data

Usage

replication.SDA(table.Symbolic, u=2, method="SClust", S=10, fixedAsample=NULL, ...)

Arguments

table.Symbolic	symbolic data table
u	number of clusters given arbitrarily
method	clustering method: "SClust" (default), "DClust", "single", "complete", "average", "mcquitty", "median", "centroid", "ward", "pam", "diana"
S	the number of simulations used to compute average adjusted Rand index
fixedAsample	if NULL A sample is generated randomly, otherwise this parameter contains object numbers arbitrarily assigned to A sample
...	additional argument passed to dist_SDA function

Details

See file ../doc/replicationSDA_details.pdf for further details

Value

A	3-dimensional array containing data matrices for A sample of objects in each simulation (first dimension represents simulation number, second - object number, third - variable number)
B	3-dimensional array containing data matrices for B sample of objects in each simulation (first dimension represents simulation number, second - object number, third - variable number)
medoids	3-dimensional array containing matrices of observations on u representative objects (medoids) for A sample of objects in each simulation (first dimension represents simulation number, second - cluster number, third - variable number)
clusteringA	2-dimensional array containing cluster numbers for A sample of objects in each simulation (first dimension represents simulation number, second - object number)
clusteringB	2-dimensional array containing cluster numbers for B sample of objects in each simulation (first dimension represents simulation number, second - object number)
clusteringBB	2-dimensional array containing cluster numbers for B sample of objects in each simulation according to 4 step of replication analysis procedure (first dimension represents simulation number, second - object number)
cRand	value of average adjusted Rand index for S simulations

Author(s)

Andrzej Dudek [e-mail], Justyna Wilk [e-mail] Department of Econometrics and Computer Science,Wroclaw University of Economics, Poland http://keii.ue.wroc.pl/symbolicDA/

References

Breckenridge, J.N. (2000), Validating cluster analysis: consistent replication and symmetry, "Multivariate Behavioral Research", 35 (2), 261-285. Available at: doi: 10.1207/S15327906MBR3502_5.

Gordon, A.D. (1999), Classification, Chapman and Hall/CRC, London. ISBN 9781584880134.

Hubert, L., Arabie, P. (1985), Comparing partitions, "Journal of Classification", no. 1, 193-218. Available at: doi: 10.1007/BF01908075.

Milligan, G.W. (1996), Clustering validation: results and implications for applied analyses, In P. Arabie, L.J. Hubert, G. de Soete (Eds.), Clustering and classification, World Scientific, Singapore, 341-375. ISBN 9789810212872.

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

dist_SDA, SClust, DClust; hclust in stats library; pam in cluster library; replication.Mod in clusterSim library

Examples

#data("cars",package="symbolicDA")
#set.seed(123)
#w<-replication.SDA(cars, u=3, method="SClust", S=10)
#print(w)

symbolicDA documentation built on Feb. 16, 2023, 6 p.m.