clustering with NA values in R

Question

I was surprised to find out that clara from library(cluster) allows NAs. But function documentation says nothing about how it handles these values.

So my questions are:

1. How clara handles NAs?
2. Can this be somehow used for kmeans (Nas not allowed)?

[Update] So I did found lines of code in clara function:

inax <- is.na(x)
valmisdat <- 1.1 * max(abs(range(x, na.rm = TRUE)))
x[inax] <- valmisdat

which do missing value replacement by valmisdat. Not sure I understand the reason to use such formula. Any ideas? Would it be more "natural" to treat NAs by each column separately, maybe replacing with mean/median?

Answer 1

Although not stated explicitly, I believe that NA are handled in the manner described in the ?daisy help page. The Details section has:

In the daisy algorithm, missing values in a row of x are not included in the dissimilarities involving that row.

Given internally the same code will be being used by clara() that is how I understand that NAs in the data can be handled - they just don't take part in the computation. This is a reasonably standard way of proceeding in such cases and is for example used in the definition of Gower's generalised similarity coefficient.

Update The C sources for clara.c clearly indicate that this (the above) is how NAs are handled by clara() (lines 350-356 in ./src/clara.c):

    if (has_NA && jtmd[j] < 0) { /* x[,j] has some Missing (NA) */
        /* in the following line (Fortran!), x[-2] ==> seg.fault
           {BDR to R-core, Sat, 3 Aug 2002} */
        if (x[lj] == valmd[j] || x[kj] == valmd[j]) {
        continue /* next j */;
        }
    }

Answer 2

Not sure if kmeans can handle missing data by ignoring the missing values in a row.

There are two steps in kmeans;

1. calculating the distance between an observation and original cluster mean.
2. updating the new cluster mean based on the newly calculated distances.

When we have missing data in our observations: Step 1 can be handled by adjusting the distance metric appropriately as in the clara/pam/daisy package. But Step 2 can only be performed if we have some value for each column of an observation. Therefore imputing might be the next best option for kmeans to deal missing data.