Naively I imagine giving a range, divide up into equal increments and
calculate all relevant cluster numbers. It would take the order of (# of
increments)**2 time to do but it seems to me that for a given corpus
you wouldn't need to do this very often (actually you only need 1/2 this
data). You would get a 3-d surface/histogram with magnitude = # of
clusters, x and y = t1 and t2. Then search this data for local maxes,
mins and inflection points. I'm not sure what this data would look like
-- hence the "naively" disclaimer at the start. It is certainly a large
landscape to search by hand.
Your method only looks at the diagonal (t1==t2)and maybe that is the
most interesting part, in which case the calculations are much quicker.
Ultimately I'm interested in finding a better way to do hierarchical
clustering. Information very often has a natural hierarchy but the usual
methods produce spotty results. If we had a reasonable canopy estimator
we could employ it at each level on the subset of the corpus being
clustered. Doing this by hand quickly becomes prohibitive given that the
number of times you have to estimate canopy values increases
exponentially with each level of hierarchy
Even a mediocre estimator would likely be better that picking k out of
the air. And the times it would fail to produce would also tell you
something about your data.
On 5/10/12 6:12 AM, Jeff Eastman wrote:
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.
