I am working through the following tutorial about how to create clusters within a dataset. [The tutorial can be found here](http://www.sthda.com/english/wiki/determining-the-optimal-number-of-clusters-3-must-known-methods-unsupervised-machine-learning).

Using the following code, we can create a plot where we visualise the three clusters which have been formed.

```
library(factoextra)
library(cluster)
library(NbClust)
#
data(iris)
head(iris)
#
iris.scaled <- scale(iris[, -5])
head(iris.scaled)
#
# K-means clustering
set.seed(123)
km.res <- kmeans(iris.scaled, 3, nstart = 25)

#
# lets inspect the output here
kmeans(iris.scaled, 3, nstart = 25)
#
#

# k-means group number of each observation
km.res$cluster
#
# Visualize k-means clusters
fviz_cluster(km.res, data = iris.scaled, geom = "point",
             stand = FALSE, frame.type = "norm") + theme_bw()

```

While, it looks fantastic that we have found three clusters within the dataset, I cannot understand what we have plotted on our x and y axis, what is the Dim1 and Dim2 and what does the 73 and 22.9% have to do with anything.

We plot the output from the `kmeans` command, where I assume it calls upon the `Cluster means:`??