I'm asking this question with a Computer Vision background (my stat background is limited). I have a set of data that measure the edge strength (based on color gradient) of a set of colors.
Since these was some sort of similarity measure, based on the answer to this question I posted earlier, I ran HAC and got some results. Then, I calculate the mean edge strength of each cluster with its std. deviation.
(mean) (#) (std. dev)
13.9970 11.0000 1.2536 --- medium strength edge strength cluster
21.6859 1.0000 0
22.3964 1.0000 0
23.1407 1.0000 0
25.6370 1.0000 0
26.1904 1.0000 0
19.5371 2.0000 0.2155
3.2880 7.0000 1.7849 --- very low edge strength cluster
25.3500 2.0000 1.4310
These results make sense naturally, as the ones with low edge strength are grouped together.
My actual requirement, is to get a number of clusters (k), that would nicely mix the low edge strength ones with higher edge ones, so that the colors with low edge strength will have a good chance of getting recognized as an edge. I was wondering is there a way to define some cost function so that I can get these sort of clusters, instead of just getting clusters that group based on overall edge strength.
Essentially, if I want 3 clusters, I'd like the best possible 3 clusters (within a certain number of iterations) such that the edge strengths are mixed to get the best possible results.
I think understand that I would have to define my own cost function for this to pre-process the data. But, I'm at a loss as to how to define such a cost function. Any help here is much appreciated.
Note: The actual data after being pre-processed to a symmetrical square matrix is available at this link, for anyone who's interested. I directly feed this to HAC through the matlab linkage function using the 'single' method. matlab hac documentation
