clusterSim (version 0.51-5)

cluster.Sim: 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",
	outputCsv="",outputCsv2="",normalizations=NULL,
	distances=NULL,methods=NULL)

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

time

time of all calculations for path

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 or C: no. of objects - 2)

maxClusterNo

maximal number of clusters, between 2 and no. of objects - 1 (for G3 or C: 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" - G3 index,"C" - C index, "KL" - Krzanowski & Lai index

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

Author

Marek Walesiak , Andrzej Dudek

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

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 ../doc/clusterSim_details.pdf for further details

References

Everitt, B.S., Landau, E., Leese, M. (2001), Cluster analysis, Arnold, London. ISBN 9780340761199.

Gatnar, E., Walesiak, M. (Eds.) (2004), Metody statystycznej analizy wielowymiarowej w badaniach marketingowych [Multivariate statistical analysis methods in marketing research], Wydawnictwo AE, Wroclaw, p. 338.

Gordon, A.D. (1999), Classification, Chapman & Hall/CRC, London. ISBN 9781584880134.

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. Available at: tools:::Rd_expr_doi("10.1007/BF02294245").

Milligan, G.W., Cooper, M.C. (1988), A study of standardization of variables in cluster analysis, "Journal of Classification", vol. 5, 181-204. Available at: tools:::Rd_expr_doi("10.1007/BF01897163").

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.C, index.S, index.KL, hclust, dist,

Examples

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