The reason I use T1==T2 is that T2 is the only threshold that determines
the number of clusters. T1 affects how many adjacent points are
considered in the centroid calculations. So you could simplify your
histogram analysis to 2-d without affecting #clusters.
Hierarchical clustering is one way to think about the clustering of
information that we have just recently added to Mahout. Any experiences
you can share with its application would be valuable.
On 5/10/12 12:20 PM, Pat Ferrel wrote:
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.
