cluster.Sim {clusterSim}R Documentation

Determination of optimal clustering procedure for a data set

Description

Determination of optimal clustering procedure for a data set by varying all combinations of normalization formulas, distance measures, and clustering methods

Usage

cluster.Sim (x,p,minClusterNo,maxClusterNo,icq="S",outputHtml="",
        outputCsv="",outputCsv2="",normalizations=NULL,
        distances=NULL,methods=NULL)

Arguments

x matrix or dataset
p path of simulation: 1 - ratio data, 2 - interval or mixed (ratio & interval) data, 3 - ordinal data, 4 - nominal data, 5 - binary data, 6 - ratio data without normalization, 7 - interval or mixed (ratio & interval) data without normalization, 8 - ratio data with k-means, 9 - interval or mixed (ratio & interval) data with k-means
minClusterNo minimal number of clusters, between 2 and no. of objects - 1 (for G3: no. of objects - 2)
maxClusterNo maximal number of clusters, between 2 and no. of objects - 1 (for G3: no. of objects - 2; for KL: no. of objects - 3), greater or equal minClusterNo
icq Internal cluster quality index, "S" - Silhouette,"G1" - Calinski & Harabasz index, "G2" - Baker & Hubert index ,"G3" - Hubert & Levine index, "KL" - Krzanowski & Lai index
outputHtml optional, name of html file with results
outputCsv optional, name of csv file with results
outputCsv2 optional, name of csv (comma as decimal point sign) file with results
normalizations optional, vector of normalization formulas that should be used in procedure
distances optional, vector of distance measures that should be used in procedure
methods optional, vector of classification methods that should be used in procedure

Details

Parameter normalizations for each path may be the subset of the following values

path 1: "n6" to "n11" (if measurement scale of variables is ratio and transformed measurement scale of variables is ratio) or "n1" to "n5" (if measurement scale of variables is ratio and transformed measurement scale of variables is interval)

path 2: "n1" to "n5"

path 3 to 7 : "n0"

path 8: "n1" to "n11"

path 9: "n1" to "n5"

Parameter distances for each path may be the subset of the following values

path 1: "d1" to "d7" (if measurement scale of variables is ratio and transformed measurement scale of variables is ratio) or "d1" to "d5" (if measurement scale of variables is ratio and transformed measurement scale of variables is interval)

path 2: "d1" to "d5"

path 3: "d8"

path 4: "d9"

path 5: "b1" to "b10"

path 6: "d1" to "d7"

path 7: "d1" to "d5"

path 8 and 9: N.A.

Parameter methods for each path may be the subset of the following values

path 1 to 7 : "m1" to "m8"

path 8: "m9"

path 9: "m9"

See file $R_HOME\library\clusterSim\pdf\clusterSim_details.pdf for further details

Value

result optimal value of icq for all classifications
normalization normalization used to obtain optimal value of icq
distance distance measure used to obtain optimal value of icq
method clustering method used to obtain optimal value of icq
classes number of clusters for optimal value of icq
optClustering clustering vector for optimal value of icq
optClusteringDescription description of clusters for optimal value of icq (see cluster.Description)
time time of all calculations for path

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

Everitt, B.S., Landau, E., Leese, M. (2001), Cluster analysis, Arnold, London.

Gatnar, E., Walesiak, M. (Eds.) (2004), Metody statystycznej analizy wielowymiarowej w badaniach marketingowych [Multivariate statistical analysis methods in marketing research], Wydawnictwo AE, Wroclaw.

Gordon, A.D. (1999), Classification, Chapman & Hall/CRC, London.

Milligan, G.W., Cooper, M.C. (1985), An examination of procedures of determining the number of cluster in a data set, "Psychometrika", vol. 50, no. 2, 159-179.

Milligan, G.W., Cooper, M.C. (1988), A study of standardization of variables in cluster analysis, "Journal of Classification", vol. 5, 181-204.

Walesiak, M., Dudek, A. (2006), Symulacyjna optymalizacja wyboru procedury klasyfikacyjnej dla danego typu danych - oprogramowanie komputerowe i wyniki badan, Prace Naukowe AE we Wroclawiu, 1126, 120-129.

Walesiak, M., Dudek, A. (2007), Symulacyjna optymalizacja wyboru procedury klasyfikacyjnej dla danego typu danych - charakterystyka problemu, Zeszyty Naukowe Uniwersytetu Szczecinskiego nr 450, 635-646.

See Also

data.Normalization, dist.GDM, dist.BC, dist.SM, index.G1, index.G2,

index.G3, index.S, index.KL, hclust, dist,

Examples

library(clusterSim)
data(data_ratio)
cluster.Sim(data_ratio, 1, 2, 10, "G1", 
outputCsv="results1")
data(data_interval)
cluster.Sim(data_interval, 2, 2, 15, "G1", 
outputHtml="results2")
data(data_ordinal)
cluster.Sim(data_ordinal, 3, 2, 12,"G2", 
outputCsv2="results3")
data(data_nominal)
cluster.Sim(data_nominal, p=4, 2, 5, icq="G3",
outputHtml="results4", methods=c("m2","m3","m5"))
data(data_binary)
cluster.Sim(data_binary, p=5, 2, 6, icq="S", 
outputHtml="results5", distances=c("b1","b3","b6"))
data(data_ratio)
cluster.Sim(data_ratio, 1, 2, 10,"G1",
outputCsv="results6",normalizations=c("n1","n3"),
distances=c("d2","d5"),methods=c("m5","m3","m1"))

[Package clusterSim version 0.36-4 Index]