pamCluster: Partitioning Around Medoids (PAM) Clustering

In Evacluster: Evaluation Clustering Methods for Disease Subtypes Diagnosis

Partitioning Around Medoids (PAM) Clustering

Description

This function partitions (clustering) of the data into k clusters "around medoids". In contrast to the k-means algorithm, this clustering methods chooses actual data points as centers

Usage

pamCluster(data = NULL, ...)

Arguments

data	A Data set
...	k: The number of clusters

Value

A list of cluster labels and a R object of class "pam cluster"

Examples

library(datasets)
data(iris)

rndSamples <- sample(nrow(iris),100)
trainData <- iris[rndSamples,]
testData <- iris[-rndSamples,]

cls <- pamCluster(trainData[,1:4],3)

Evacluster documentation built on April 1, 2022, 9:07 a.m.