No, I mean the area. If all the vectors fit in a AxBxC sized box, and
you expect about 10 clusters, you could make an initial guess that the
clusters will be (A/10)xBxC in size and you could try T1=(A/10)*B*C.
I've no idea how well this would work in practice... probably not very
well.
On 04/27/2011 01:50 PM, Camilo Lopez wrote:
By area of the space you mean just the total number of vectors I'm using?
On 2011-04-27, at 4:46 PM, Paul Mahon wrote:
If you have a guess at how many clusters you want you could take the total area
of the space and divide by the number of clusters to get an initial guess of T2
or T1. That might work to get you started, depending on the distribution.
On 04/27/2011 12:39 PM, Camilo Lopez wrote:
I'm using Canopy as first step for K-means clustering, is there any
algorithmic, or even a good heuristic to estimate good T1 and T2 from the
vectorized data?
