I know of no reliable ways to avoid some iteration in setting the T
values for Canopy but T1 really has no impact on the number of clusters
so setting T1==T2 and experimenting with T2 will reduce your search space.
On 2/1/13 6:29 AM, Chris Harrington wrote:
Seems my lack of any clusters what so ever was my own fault, wasn't pointing at
the correct directory.
Though I would still like to find some good material on this topic of figuring
out t1 and t2, is it just trial and error or are there specific features of my
data set that I can look at to infer at least marginally good values as a
starting point?
On 31 Jan 2013, at 22:37, Stefan Kreuzer wrote:
Hi Chris,
I am also experimenting with CC. For me chosing CosineDistanceMeasure and values
very close to 1 (>0.96) with T2 being only a little smaller than T1 led to
reasonable values for k. Although this puzzles me too, I just asked a another
question because of this.
-----Ursprüngliche Mitteilung-----
Von: Chris Harrington <[email protected]>
An: user <[email protected]>
Verschickt: Do, 31 Jan 2013 7:22 pm
Betreff: Figuring out good values for t1 and t2 for canopy
Hi all,
I'm trying to run canopy clustering before means and I can't seem to get a value
for t1 and t2 that give me any results.
No matter what values I use it results in no clusters.
This is probably due to a severe lack of knowledge on the subject on my part so
can anyone point me toward some good resources to read up on the topic of
choosing a distance measure and a t1 and t2 for that measure?
