data.Normalization {clusterSim}R Documentation

Types of variable normalization formulas

Description

Types of variable normalization formulas

Usage

data.Normalization (x,type="n0")

Arguments

x vector, matrix or dataset
type type of normalization:

n0 - without normalization
n1 - standardization ((x-mean)/sd)
n2 - Weber standardization ((x-Me)/MAD)
n3 - unitization ((x-mean)/range)
n4 - unitization with zero minimum ((x-min)/range)
n5 - normalization in range <-1,1> ((x-mean)/max(abs(x-mean)))
n6 - quotient transformation (x/sd)
n7 - quotient transformation (x/range)
n8 - quotient transformation (x/max)
n9 - quotient transformation (x/mean)
n10 - quotient transformation (x/sum)
n11 - quotient transformation (x/sqrt(SSQ))

Details

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

Value

Normalized data

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

Gatnar, E., Walesiak, M. (Eds.) (2004), Metody statystycznej analizy wielowymiarowej w badaniach marketingowych [Multivariate statistical analysis methods in marketing research], Wydawnictwo AE, Wroclaw, 35-38.

Jajuga, K., Walesiak, M. (2000), Standardisation of data set under different measurement scales, In: R. Decker, W. Gaul (Eds.), Classification and information processing at the turn of the millennium, Springer-Verlag, Berlin, Heidelberg, 105-112.

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

See Also

cluster.Sim

Examples

library(clusterSim)
data(data_ratio)
z1 <- data.Normalization(data_ratio, "n1")
z2 <- data.Normalization(data_ratio, type="n2")
z8 <- data.Normalization(data_ratio, type="n8")

[Package clusterSim version 0.36-4 Index]