HINoV.Mod {clusterSim}R Documentation

Modification of Carmone, Kara \& Maxwell Heuristic Identification of Noisy Variables (HINoV) method

Description

Modification of Heuristic Identification of Noisy Variables (HINoV) method

Usage

HINoV.Mod (x, type="metric", s = 2, u, distance=NULL, 
	method = "kmeans", Index ="cRAND")

Arguments

x

data matrix

type

"metric" (default) - all variables are metric (ratio, interval), "nonmetric" - all variables are nonmetric (ordinal, nominal) or vector containing for each variable value "m"(metric) or "n"(nonmetric) for mixed variables (metric and nonmetric), e.g. type=c("m", "n", "n", "m")

s

for metric data only: 1 - ratio data, 2 - interval or mixed (ratio & interval) data

u

number of clusters (for metric data only)

distance

NULL for kmeans method (based on data matrix) and nonmetric data

for ratio data: "d1" - Manhattan, "d2" - Euclidean, "d3" - Chebychev (max), "d4" - squared Euclidean, "d5" - GDM1, "d6" - Canberra, "d7" - Bray-Curtis

for interval or mixed (ratio & interval) data: "d1", "d2", "d3", "d4", "d5"

method

NULL for nonmetric data

clustering method: "kmeans" (default) , "single", "ward.D", "ward.D2", "complete", "average", "mcquitty", "median", "centroid", "pam"

Index

"cRAND" - corrected Rand index (default); "RAND" - Rand index

Details

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

Value

parim

m x m symmetric matrix (m - number of variables). Matrix contains pairwise corrected Rand (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)

Marek Walesiak marek.walesiak@ue.wroc.pl, Andrzej Dudek andrzej.dudek@ue.wroc.pl

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

References

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. (2005), Variable selection for cluster analysis - approaches, problems, methods, Plenary Session of the Committee on Statistics and Econometrics of the Polish Academy of Sciences, 15 March, Wroclaw.

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: 10.1007/978-3-540-78246-9_11

See Also

hclust, kmeans, dist, dist.GDM, dist.BC, dist.SM, cluster.Sim

Examples

# for metric data
library(clusterSim)
data(data_ratio)
r1<- HINoV.Mod(data_ratio, type="metric", s=1, 4, method="kmeans",
     Index="cRAND")
print(r1$stopri)
plot(r1$stopri[,2],xlab="Variable number", ylab="topri",
xaxt="n", type="b")
axis(1,at=c(1:max(r1$stopri[,1])),labels=r1$stopri[,1])

# for nonmetric data
library(clusterSim)
data(data_nominal)
r2<- HINoV.Mod (data_nominal, type="nonmetric", Index = "cRAND")
print(r2$stopri)
plot(r2$stopri[,2], xlab="Variable number", ylab="topri",
xaxt="n", type="b")
axis(1,at=c(1:max(r2$stopri[,1])),labels=r2$stopri[,1])

# for mixed data
library(clusterSim)
data(data_mixed)
r3<- HINoV.Mod(data_mixed, type=c("m","n","m","n"), s=2, 3, distance="d1",
     method="complete", Index="cRAND")
print(r3$stopri)
plot(r3$stopri[,2], xlab="Variable number", ylab="topri",
xaxt="n", type="b")
axis(1,at=c(1:max(r3$stopri[,1])),labels=r3$stopri[,1])


[Package clusterSim version 0.49-2 Index]