No, the issue was discussed but never reached critical mass. I typically
do a binary search to find the best value setting T1==T2 and then tweak
T1 up a bit. For feeding k-means, this latter step is not so important.
If you could figure out a way to automate this we would be interested.
Conceptually, using the RandomSeedGenerator to sample a few vectors and
comparing them with your chosen DistanceMeasure would give you a hint at
the T-value to begin the search. A utility to do that would be a useful
contribution.
On 5/9/12 8:36 PM, Pat Ferrel wrote:
Some thoughts on https://issues.apache.org/jira/browse/MAHOUT-563
Did anything ever get done with this? Ted mentions limited usefulness.
This may be true but the cases he mentions as counter examples are
also not very good for using canopy ahead of kmeans, no? That info
would be a useful result. To use canopies I find myself running it
over and over trying to see some inflection in the number of clusters.
Why not automate this? Even if the data shows nothing, that is itself
an answer of value and it would save a lot of hand work to find out
the same thing.
