HINoV.SDA: Modification of HINoV method for symbolic data

In symbolicDA: Analysis of Symbolic Data

Modification of HINoV method for symbolic data

Description

Carmone, Kara and Maxwell's Heuristic Identification of Noisy Variables (HINoV) method for symbolic data

Usage

HINoV.SDA(table.Symbolic, u=NULL, distance="H", Index="cRAND",method="pam",...)

Arguments

table.Symbolic	symbolic data table
u	number of clusters
distance	symbolic distance measure as parameter type in dist_SDA
method	clustering method: "single", "ward", "complete", "average", "mcquitty", "median", "centroid", "pam" (default), "SClust", "DClust"
Index	"cRAND" - adjusted Rand index (default); "RAND" - Rand index
...	additional argument passed to dist_SDA function

Details

For HINoV in symbolic data analysis there can be used methods based on distance matrix such as hierarchical ("single", "ward", "complete", "average", "mcquitty", "median", "centroid") and optimization methods ("pam", "DClust") and also methods based on symbolic data table ("SClust").

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

Value

parim	m x m symmetric matrix (m - number of variables). Matrix contains pairwise adjusted Rand (or Rand) indices for partitions formed by the j-th variable with partitions formed by the l-th variable
topri	sum of rows of parim
stopri	ranked values of topri in decreasing order

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

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.

Carmone, F.J., Kara, A., Maxwell, S. (1999), HINoV: a new method to improve market segment definition by identifying noisy variables, "Journal of Marketing Research", November, vol. 36, 501-509.

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

Rand, W.M. (1971), Objective criteria for the evaluation of clustering methods, "Journal of the American Statistical Association", no. 336, 846-850. Available at: doi: 10.1080/01621459.1971.10482356.

Walesiak, M., Dudek, A. (2008), Identification of noisy variables for nonmetric and symbolic data in cluster analysis, In: C. Preisach, H. Burkhardt, L. Schmidt-Thieme, R. Decker (Eds.), Data analysis, machine learning and applications, Springer-Verlag, Berlin, Heidelberg, 85-92. Available at: doi: 1007/978-3-540-78246-9_11

See Also

DClust, SClust, dist_SDA; HINoV.Symbolic, dist.Symbolic in clusterSim library; hclust in stats library; pam in cluster library

Examples

# LONG RUNNING - UNCOMMENT TO RUN
#data("cars",package="symbolicDA")
#r<- HINoV.SDA(cars, u=3, distance="U_2")
#print(r$stopri)
#plot(r$stopri[,2], xlab="Variable number", ylab="topri",
#xaxt="n", type="b")
#axis(1,at=c(1:max(r$stopri[,1])),labels=r$stopri[,1])

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