recently I came to study clustering in data-mining and I've studied sequential clustering and hierarchical clustering and k-means.

I also read about a statement that distinguishes k-means from the other two clustering technique, saying k-means is not very good at dealing with nominal attributes, but the text didn't explain this point. So far, the only difference that I can see is that for K-means, we will know in advance we will need exactly K clusters while we don't know how many clusters we need for the other two clustering methods.

So could anybody give me some idea here on why such a statement exists,i.e.,k-means has this problem when dealing with examples of nominal attributes and is there a way to overcome this?

Thanks in advance.