This vignette is designed to be used with the ppclust package. You can download the recent version of the package ‘ppclust’ from CRAN with the following command:
install.packages("ppclust")If you have already installed ‘ppclust’, you can load it into R working environment by using the following command:
library(ppclust)For visualization of the clustering results, some examples in this vignette use the functions from some cluster analysis packages such as ‘cluster’, ‘fclust’ and ‘factoextra’. Therefore, these packages should be loaded into R working environment with the following commands:
library(factoextra)
library(cluster)
library(fclust)We demonstrate FCM on the Iris data set (Anderson, 1935). It is a real data set of the four features (Sepal.Length, Sepal.Width, Petal.Length and Petal.Width) of 150 iris flowers with three species (in the last column as the class variable). This four-dimensional data set contains 50 samples each of three iris species. One of these three natural clusters (Class 1) is linearly well-separated from the other two clusters, while Classes 2 and 3 have some overlap as seen in the plot below.
data(iris)
x=iris[,-5]
xPlot the data by the classes of iris species
pairs(x, col=iris[,5])