clusterSim (version 0.49-2)

interval_normalization: Types of normalization formulas for interval-valued symbolic variables

Description

Types of normalization formulas for interval-valued symbolic variables

Usage

interval_normalization(x,dataType="simple",type="n0",y=NULL,...)

Arguments

x

matrix dataset or symbolic table object

dataType

Type of symbolic data table passed to function,

'sda' - full symbolicDA format object;

'simple' - three dimensional array with lower and upper bound of intervals in third dimension;

'separate_tables' - lower bounds of intervals in x, upper bounds in y;

'rows' - lower and upper bound of intervals in neighbouring rows;

'columns' - lower and upper bound of intervals in neighbouring columns

type

type of normalization:

y

matrix or dataset with upper bounds of intervals if argument dataType is uuqual to "separate_tables"

...

arguments passed to sum, mean, min sd, mad and other aggregation functions. In particular: na.rm - a logical value indicating whether NA values should be stripped before the computation

Value

Normalized data

References

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. Available at: 10.1007/978-3-642-57280-7_11.

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: 10.1007/BF01897163.

Walesiak, M. (2014), Przeglad formul normalizacji wartosci zmiennych oraz ich wlasnosci w statystycznej analizie wielowymiarowej [Data normalization in multivariate data analysis. An overview and properties], "Przeglad Statystyczny" ("Statistical Review"), vol. 61, no. 4, 363-372. Available at: http://keii.ue.wroc.pl/pracownicy/mw/2014_Walesiak_Przeglad_Statystyczny_z_4.pdf.

Walesiak, M., Dudek, A. (2017), Selecting the Optimal Multidimensional Scaling Procedure for Metric Data with R Environment, <U+201E>STATISTICS IN TRANSITION new series<U+201D>, September, Vol. 18, No. 3, pp. 521-540. Available at: http://stat.gov.pl/en/sit-en/issues-and-articles-sit/.

See Also

data.Normalization

Examples

# NOT RUN {
library(clusterSim)
data(data_symbolic_interval_polish_voivodships)
n<-interval_normalization(data_symbolic_interval_polish_voivodships,dataType="simple",type="n2")
plotInterval(n$simple)
# }