From the thread I couldn't tell if your data are categorical or ordinal (or perhaps a mixture of both). Nor if you are using 'cluster' in a colloquial or technical sense.
If the data are categorical, I would certainly take a look at: http://www.jmp.com/support/help/Multiple_Correspondence_Analysis.shtml. If you have a mix of categorical and continuous and/or ordinal variables, some would advocate making the latter variables categorical so that they too can be included in the MCA. Although this process loses information, it can sometimes be better than not including the variables at all. And, as you may have found through your own searches, some advocate using the output from MCA as the input to clustering, and you could certainly try this too.
Ultimately, though, any 'best', or even workable method depends on the specific objectives you have set (how would the results actually be used?), and how well the data you have to hand does (or does not . . . ) support meeting these objectives. It goes without saying that simple graphical and descriptive summaries will help point you on the right direction.
