R code for Fuzzy C-Means Clustering

 R code for Fuzzy C-Means Clustering

For using fuzzy c-means you need to install the cluster package for clustering algorithms and use fanny() function for fuzzy c-means

	#Fuzzy c-means clustering
	library(factoextra) # Custom visualizations for clusters
	library(tidyverse) # Data handling
	library(cluster) # Clustering algorithms
	# importing the data
	data <- read_csv('iris.csv')
	data <- data[,-1]
	# Modify the data,
	# create unique names for each row by adding species type to row number
	# then add column species to rownames
	data <- data %>%
	group_by(Species) %>%
	mutate(spec_idx = row_number()) %>%
	unite('Species', Species, spec_idx, sep="-", remove=TRUE) %>%
	column_to_rownames('Species')
	# fanny() function from cluster packages is
	# for fuzzy c-means clustering algorithm
	# we are also defining 3 clusters to create.
	res.fanny <- fanny(data,3)
	# Look at the membership coefficients of first 7 element
	head(res.fanny['membership'], 7)
	# Cluster plot
	fviz_cluster(res.fanny, ellipse.type = "convex",
	palette = c("#00AFBB", "#E7B800", "#FC4E07"),
	ggtheme = theme_minimal(),
	legend = "right")

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Output:

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