HINoV.Mod {clusterSim} | R Documentation |
Modification of Heuristic Identification of Noisy Variables (HINoV) method
HINoV.Mod (x, type="metric", s = 2, u, distance=NULL, method = "kmeans", Index ="cRAND")
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", "complete", "average", "mcquitty", "median", "centroid", "pam" |
Index |
"cRAND" - corrected Rand index (default); "RAND" - Rand index |
See file $R_HOME\library\clusterSim\pdf\HINoVMod_details.pdf for further details
parim |
m x m symmetric matrix (m - number of variables). Matrix contains pairwise corrected Rand (Rand) indices for partitions formed by the j-th variable with partitions formed by the l-th variable |
topri |
sum of rows of parim |
stopri |
ranked values of topri in decreasing order |
Marek Walesiak marek.walesiak@ue.wroc.pl, Andrzej Dudek andrzej.dudek@ue.wroc.pl
Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/clusterSim
Carmone, F.J., Kara, A., Maxwell, S. (1999), HINoV: a new method to improve market segment definition by identifying noisy variables, "Journal of Marketing Research", November, vol. 36, 501-509.
Hubert, L.J., Arabie, P. (1985), Comparing partitions, "Journal of Classification", no. 1, 193-218.
Rand, W.M. (1971), Objective criteria for the evaluation of clustering methods, "Journal of the American Statistical Association", no. 336, 846-850.
Walesiak, M. (2005), Variable selection for cluster analysis - approaches, problems, methods, Plenary Session of the Committee on Statistics and Econometrics of the Polish Academy of Sciences, 15 March, Wroclaw.
hclust
, kmeans
, dist
, dist.GDM
, dist.BC
, dist.SM
,
cluster.Sim
# for metric data library(clusterSim) data(data_ratio) r1<- HINoV.Mod(data_ratio, type="metric", s=1, 4, method="kmeans", Index="cRAND") print(r1$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])