normalization: Apply Normalization Techniques to the Dataset

In hclusteasy: Determining Hierarchical Clustering Easily

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")

hclusteasy documentation built on June 22, 2024, 9:48 a.m.