cluster.Sim: Determination of optimal clustering procedure for a data set

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

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)

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

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

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

Author(s)

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

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

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: \Sexpr[results=rd]{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: \Sexpr[results=rd]{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

#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"))

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