Ordering clustered points using Kmeans and R

Question

I have set of data (of 5000 points with 4 dimensions) that I have clustered using kmeans in R. I want to order the points in each cluster by their distance to the center of that cluster. Very simply, the data looks like this (I am using a subset to test out various approaches): ``` id Ans Acc Que Kudos 1 100 100 100 100 2 85 83 80 75 3 69 65 30 29 4 41 45 30 22 5 10 12 18 16 6 10 13 10 9 7 10 16 16 19 8 65 68 100 100 9 36 30 35 29 10 36 30 26 22 ``` Firstly, I used the following method to cluster the dataset into 2 clusters: ``` (result <- kmeans(data, 2)) ``` This returns a kmeans object that has the following methods: cluster, centers etc. But I cannot figure out how to compare each point and produce an ordered list. Secondly, I tried the seriation approach as suggested by another SO user here I use these commands: ``` clus <- kmeans(scale(x, scale = FALSE), centers = 3, iter.max = 50, nstart = 10) mns <- sapply(split(x, clus$cluster), function(x) mean(unlist(x))) result <- dat[orde…

Accepted Answer

Here is an example that does what you ask, using the first example from `?kmeans`. It is probably not terribly efficient, but is something to build upon.

```
#Taken straight from ?kmeans
x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2),
           matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2))
colnames(x) <- c("x", "y")
cl <- kmeans(x, 2)

x <- cbind(x,cl = cl$cluster)

#Function to apply to each cluster to 
# do the ordering
orderCluster <- function(i,data,centers){
    #Extract cluster and center
dt <- data[data[,3] == i,]
ct <- centers[i,]

#Calculate distances
dt <- cbind(dt,dist = apply((dt[,1:2] - ct)^2,1,sum))
    #Sort
dt[order(dt[,4]),]
}

do.call(rbind,lapply(sort(unique(cl$cluster)),orderCluster,data = x,centers = cl$centers))
```

Ordering clustered points using Kmeans and R

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