HINoV.Mod: Modification of Carmone, Kara & Maxwell Heuristic...

In clusterSim: Searching for Optimal Clustering Procedure for a Data Set

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 [e-mail], Andrzej Dudek [e-mail]

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland

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: \Sexpr[results=rd]{tools:::Rd_expr_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: \Sexpr[results=rd]{tools:::Rd_expr_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: \Sexpr[results=rd]{tools:::Rd_expr_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])

clusterSim documentation built on Sept. 30, 2024, 9:15 a.m.