rangenorm: range normalization
In dprep: Data Pre-Processing and Visualization Functions for Classification

Description Usage Arguments Details Value Author(s) References Examples

Performs several methods of range normalization.

1 2	rangenorm(data, method = c("znorm", "mmnorm", "dscale","signorm", "softnorm"), superv=TRUE)

`data`	The name of the dataset to be normalized
`method`	The discretization method to be used:"znorm", "mmnrom", "decscale", "signorm", "softmaxnorm"
`superv`	superv=T for supervised data, that data including the class labels in the last column. if superv=F means that the data to be used is unsupervised.

In the znorm normalization, the mean of each attribute of the transformed set of data points is reduced to zero by subtracting the mean of each attribute from the values of the attributes and dividing the difference by the standard deviation of the attribute. Uses the function scale found in the base library.

Min-max normalization (mmnorm) subtracts the minimum value of an attribute from each value of the attribute and then divides the difference by the range of the attribute. These new values are multiplied by the new range of the attribute and finally added to the new minimum value of the attribute. These operations transform the data into a new range, generally [0,1].

The decscale normalization applies decimal scaling to a matrix or dataframe. Decimal scaling transforms the data into [-1,1] by finding k such that the absolute value of the maximum value of each attribute divided by 10\^k is less than or equal to 1.

In the sigmoidal normalization (signorm) the input data is nonlinearly transformed into [-1,1] using a sigmoid function. The original data is first centered about the mean, and then mapped to the almost linear region of the sigmoid. Is especially appropriate when outlying values are present.

The softmax normalization is so called because it reaches "softly" towards maximum and minimum value, never quite getting there. The transformation is more or less linear in the middle range, and has a nonlinearity at both ends. The output range covered is [0,1]. The algorithm removes the classes of the dataset before normalization and replaces them at the end to form the matrix again.

A matriz containing the discretized data.

Caroline Rodriguez and Edgar Acuna

Caroline Rodriguez (2004). An computational environmnent for data preprocessing in supervised classification. Master thesis. UPR-Mayaguez

Hann, J., Kamber, M. (2000). Data Mining: Concepts and Techniques. Morgan Kaufman Publishers.

#----Several methods of range normalization ----
data(bupa)
bupa.znorm=rangenorm(bupa,method="znorm",superv=TRUE)
bupa.mmnorm=rangenorm(bupa,method="mmnorm",superv=TRUE)
bupa.decs=rangenorm(bupa,method="dscale",superv=TRUE)
bupa.signorm=rangenorm(bupa,method="signorm",superv=TRUE)
bupa.soft=rangenorm(bupa,method="softnorm",superv=TRUE)
#----Plotting to see the effect of the normalization----
op=par(mfrow=c(2,3))
plot(bupa[,1])
plot(bupa.znorm[,1])
plot(bupa.mmnorm[,1])
plot(bupa.decs[,1])
plot(bupa.signorm[,1])
plot(bupa.soft[,1])
par(op)

Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found