I hope this is an appropriate forum for this question, and I asked on math.stackexchange as well. If it doesn't belong, I don't mind closing this. If my questions is not clear, please just let me know and I'll try to add information/explanation where I can.
Say I have a set of data points that I run through a FLAME clustering algorithm, and I get my set of cluster supporting objects, and the fuzzy memberships among them.
Next, let's say I get an additional set of data, for argument's sake, let's say it doubles the amount of data. If I want to add that to my clustering, without having to reprocess the old data (i.e. just using the cluster supporting objects, and adding these new observations to the cluster), will I be introducing some kind of bias? To get the most "accurate" picture, would it be better to reprocess ALL the data, re-establish CSO's, re-compute distances, etc.?
To add to the complexity, let's say I'm going to be periodically adding similar quantities of data points. Does the answer to the above change?
And I guess a final question - is there a clustering algorithm (or family of algorithms) that is not subject to extreme bias in the beginning, such that added data points can be added relatively pain-free? I'm sort of thinking that FLAME clustering, with its nice fuzziness properties is a reasonable way of going about this, but any more suggestions would be helpful.