clustering with NA values in R

cluster-analysis, r

Solution

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 `NA`s 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 `NA`s 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 */;
        }
    }

Problem

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: - How `clara` handles NAs? - 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?

Original source