NA in clustering functions (kmeans, pam, clara). How to associate clusters to original data?

Question

I need to cluster some data and I tried kmeans, pam, and clara with R.

The problem is that my data are in a column of a data frame, and contains NAs.

I used na.omit() to get my clusters. But then how can I associate them with the original data? The functions return a vector of integers without the NAs and they don't retain any information about the original position.

Is there a clever way to associate the clusters to the original observations in the data frame? (or a way to intelligently perform clustering when NAs are present?)

Thanks

Answer 1

The output of kmeans corresponds to the elements of the object passed as argument x. In your case, you omit the NA elements, and so $cluster indicates the cluster that each element of na.omit(x) belongs to.

Here's a simple example:

d <- data.frame(x=runif(100), cluster=NA)
d$x[sample(100, 10)] <- NA
clus <- kmeans(na.omit(d$x), 5)

d$cluster[which(!is.na(d$x))] <- clus$cluster

And in the plot below, colour indicates the cluster that each point belongs to.

plot(d$x, bg=d$cluster, pch=21)

Answer 2

This code works for me, starting with a matrix containing a whole row of NAs:

DF=matrix(rnorm(100), ncol=10)
row.names(DF) <- paste("r", 1:10, sep="")
DF[3,]<-NA
res <- kmeans(na.omit(DF), 3)$cluster
res
DF=cbind(DF, 'clus'=NA)
DF[names(res),][,11] <- res
print(DF[,11])