normalization {hclusteasy} | R Documentation |
Apply Normalization Techniques to the Dataset
Description
Perform data normalization.
Usage
normalization(data, type = "n0", norm = "column", na.remove = FALSE)
Arguments
data |
Dataset in data.frame format.
|
type |
Type of normalization. Default is "n1".
n0: without normalization
n1: standardization ((x-mean)/sd)
n2: positional standardization ((x-median)/mad)
n3: unitization ((x-mean)/range)
n3a: positional unitization ((x-median)/range)
n4: unitization with zero minimum ((x-min)/range)
n5: normalization in range <-1,1> ((x-mean)/max(abs(x-mean)))
n5a: positional normalization in range <-1,1> ((x-median)/max(abs(x-median)))
n6: quotient transformation (x/sd)
n6a: positional quotient transformation (x/mad)
n7: quotient transformation (x/range)
n8: quotient transformation (x/max)
n9: quotient transformation (x/mean)
n9a: positional quotient transformation (x/median)
n10: quotient transformation (x/sum)
n11: quotient transformation (x/sqrt(SSQ))
n12: normalization ((x-mean)/sqrt(sum((x-mean)^2)))
n12a: positional normalization ((x-median)/sqrt(sum((x-median)^2)))
n13: normalization with zero being the central point ((x-midrange)/(range/2))
|
norm |
Defines whether the normalization will be done by "column" or
by "row". Default is "column".
|
na.remove |
A logical value indicating whether NA values should be
excluded before performing normalization calculations. Default is FALSE.
|
Value
Normalized dataset in data.frame
foramt.
Examples
# Load the required package
library(hclusteasy)
# Read the dataset 'iris' from the package
data("iris_uci")
# Remove the column 'Species' from the iris dataset
iris <- iris_uci[, -5]
# Apply normalization to the iris dataset
irisN <- normalization(iris, type = "n1")
[Package
hclusteasy version 0.1.0
Index]