normalization {hclusteasy}

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")