How to apply a K-means clustering algorithm after applying a PCA in the R programming language. The video also offers a preview of the upcoming Statistics Globe online course on "Principal Component Analysis (PCA): From Theory to Application in R". More details: https://statisticsglobe.com/online-course-pca-theory-application-r
R code of this video:
install.packages("factoextra") # Install & load factoextra
library("factoextra")
data(iris) # Load data
head(iris) # Print first rows of data
iris_num ＜- iris[ , 1:4] # Remove categorical variable
head(iris_num) # Print first rows of final data
my_pca ＜- prcomp(iris_num, # Perform PCA
scale = TRUE)
summary(my_pca) # Summary of explained variance
my_pca_data ＜- data.frame(my_pca$x[ , 1:2]) # Extract PC1 and PC2
head(my_pca_data) # Print first rows of PCA data
fviz_nbclust(my_pca_data, # Determine number of clusters
FUNcluster = kmeans,
method = "wss")
set.seed(123) # Set seed for reproducibility
my_kmeans ＜- kmeans(my_pca_data, # Perform k-means clustering
centers = 3)
fviz_pca_ind(my_pca, # Visualize clusters
habillage = my_kmeans$cluster,
label = "none",
addEllipses = TRUE)
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