FuzzyCluster: Fuzzy C-means Clustering Algorithm

In Evacluster: Evaluation Clustering Methods for Disease Subtypes Diagnosis

Fuzzy C-means Clustering Algorithm

Description

This function works by assigning membership to each data point corresponding to each cluster center based on the distance between the cluster center and the data point. A data object is the member of all clusters with varying degrees of fuzzy membership between 0 and 1.

Usage

FuzzyCluster(data = NULL, ...)

Arguments

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

Value

A list of cluster labels and a R object of class "fcm ppclust"

Examples

library(datasets)
data(iris)

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

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

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