You really didn't answer my last question which means I probably didn't ask it well.

Ok, sorry for the misunderstanding

Is your goal only to identify duplicate articles (typos and what not) or is it to group related articles as well?

No, it is just to group together articles with the same title (allowing misspellings).

What do you intend to do if an article can belong to more than one cluster?

It should be very improbable a typo that "converts" a title into another one (i.e. different article titles should have an edit distance greater than 5). Nevertheless I can imagine cases where this is not true (for example: Blah, blah, blah typeI VS Same Blah, blah, blah typeII. It will be very difficult to differentiate such cases (not solved yet).

Is there a subjective element to this task or can you define the criterion for clustering in black and white terms. In other words, if randomly selected 1000 articles and were to cluster them by hand - could you write code that would produce the same results (where computational time is not a factor)?

Probably there will be errors in the computational approach that can be ironed out by manual inspection. However, a reasonable small amount of errors will not degrade the final result.

I hope I answered your doubts.

Thanks in advance