I am trying to create a plot similar to this:
Here there are three clusters and all the datapoints (circles) are plotted according to their euclidean distance from the centroid. Using this image its easy to see that 5 samples from class 2 ended up in wrong clusters.
I'm running k-means using kmeans and can't figure out how to plot this type of graph.
For example purposes we can use the iris dataset.
> iri <- iris
> cl <- kmeans (iri[, 1:4], 3)
> cl
K-means clustering with 3 clusters of sizes 38, 62, 50
Cluster means:
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 6.850000 3.073684 5.742105 2.071053
2 5.901613 2.748387 4.393548 1.433871
3 5.006000 3.428000 1.462000 0.246000
Clustering vector:
[1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[40] 3 3 3 3 3 3 3 3 3 3 3 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[79] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 1 1 2 1 1 1 1 1 1 2 2 1 1
[118] 1 1 2 1 2 1 2 1 1 2 2 1 1 1 1 1 2 1 1 1 1 2 1 1 1 2 1 1 1 2 1 1 2
Within cluster sum of squares by cluster:
[1] 23.87947 39.82097 15.15100
(between_SS / total_SS = 88.4 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss"
[6] "betweenss" "size" "iter" "ifault"
Source for the image: https://github.com/michaelwsherman/winecluster The originator doesn't seem to be using kmeans though.
I suspect this might not be doable using kmeans for it does not provide distance from centroid. Is there another way to show data in this way or something similar?
