Kmeans results, is the cluster vector ordered by 'closeness"?

I ran kmeans in r with k = 20 centers and 7 scaled variables to cluster with on a data frame with n = 100K.

Using dplyr group_by I was able to view summary data for each of the 20 clusters: the mean of var 1, var 2 etc.

I thought I understood the kmeans algorithm. Start with k centers, apply each observation to the closest centroid, recalculate the mean and repeat till no more movement.

I'm confused about the resulting centroid. I assumed that it was a single number in 7 dimensional space. However when I type (R) mycluster_object$centers I get back a 20 * 7 table with a value for each cluster and variable. Example: library(dplyr) set.seed(123) myclustering <- kmeans(select(iris, -Species), centers = 3) myclustering$centers
Sepal.Length Sepal.Width Petal.Length Petal.Width
1     5.006000    3.428000     1.462000    0.246000
2     6.850000    3.073684     5.742105    2.071053
3     5.901613    2.748387     4.393548    1.433871


What exactly are these numbers?

Separately, my initial goal was to order the clusters by "closeness". This is why I thought ordering by what I thought centroids to mean would help.

1. What does myclustering$centers actually show? 2. Is there a way to order by clusters by how similar or close together they are, accounting for all the variables used? 1 Answer The numbers reported for the centers are the coordinates of each center in n-dimensional space. For example, the iris data has 3 centers in 4 D space thus the 3 rows and 4 columns. Your original problem had specified 20 centers(ie rows) and then 7 columns for each dimension. The kmeans parameter withinss (myclustering$withinss) is the measure of the cluster's sum of the square error, thus a measure of how close each point of the cluster is to the center.

To compute the distance between the centers, the dist() function is helpful.

dist(myclustering$centers) # 1 2 #2 5.017569 #3 3.356935 1.797182  thus centers 2 &3 are the closest to each other and centers 1&2 are the farthest apart. • Thanks. I'm looking at the distance matrix and wondering if there's a way of ordering the clusters. I can see visually the smallest number for each row, telling me that the corresponding cluster is closest. How could I boil this down to a single vector with which to order by clusters by closeness? – Doug Fir Oct 25 '18 at 19:14 • You can try this where n is the number of centers and d is the result from the dist function: g<-expand.grid(r=1:n, c=1:n); g<-g[g$c<g$r, ]; g$d<-as.vector(d) – Dave2e Oct 25 '18 at 19:34