Peter
> > I can't run your code because you didn't make it standalone, Thanks for the heads up, I've made a simple version of the clusterer which you can view on pastebin: http://pastebin.com/7HmAkmfj If you have time to look through my code I would be very grateful! > > but in your case that is probably not enough. > > Try something along these lines: > > > > # untested > > while len(self.clusters) > 1: > > c = self.clusters.pop() > > # find closest index > > for i, c2 in enumerate(self.clusters): > > ... > > if ...: > > closest_index = i > > closest = self.clusters.pop(closest_index) > > tmp.append(c + closest) > > if self.clusters: > > tmp.append(self.clusters[0]) I had a go at implementing something along the lines above and I'm still getting very bizarre results. There does appear to be some sort of logic to it though, if you look at the graph chart, you can see that It seems to be doing the clustering right and then forgetting to remove the old groupings providing this "cloning" effect for some cluster groups. Chart: http://img826.imageshack.us/i/clusters.png/ Thanks, James -- James Ravenscroft Funky Monkey Software - Bespoke Web and Software Solutions http://www.funkymonkeysoftware.com/
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