How to build multidimensional data in clusters

Question

I have a data set that has an instance of 6497, 12 attributes and a class variable q (quality). Class values ​​can vary from 3 to 9. Data can be downloaded in CSV format from here

I use k-mean to split data into 3 clusters

set.seed(1234)
nr <- NROW(wine$.row)
ind <- sample(nr, 0.66 * nr, replace = FALSE) #66%
w_clus3 <- kmeans(wine[ind, 2:12], 3)
matrix3 <- table(cl_predict(w_clus3, wine[-ind,2:12 ]),wine$q[-ind])

Is there a way I can use clusplot or any other visual graph to show how the data was divided between the three clusters?

I tried, but I was getting errors.

clusplot(wine[2:12], w_clus3$cluster, color=TRUE, shade=TRUE,labels=2, lines=0)

If there are too many dimensions ... how can I just show a few attributes and how they were separated in clusters.

Answer 1

, PCA? - .