I want to cluster files based on an information distance, which is obtained by comparing the compressed length of two files separately and the concatenation of the two files, using a real-world compressor like zlib. (In words: given files $x$ and $y$, how easy can you express $x$ in terms of $y$?)
This "metric" does not satisfy $d(x,x) \ne0$, and it does not always satisfy $d(x,y)=d(y,x)$, depending on the compressor used. (But I'm willing to throw away one half of the distance matrix and pretend it's triangular.)
I'm told I cannot use
hclust in R, because it always expects $d(x,x)=0$.
See my other question regarding my troubles with R (I'm only a beginner.)
What hierarchical clustering method would be suitable, preferably one that is implemented in R/Python? I only want to cluster up to 30 items right now, so scale is not an issue.