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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Validating clustering output
Date Tue, 28 Jul 2009 01:42:27 GMT
(vastly delayed response ... huge distractions competing with more than 2
minutes answers are to blame)

Grant,

For evaluating clustering for symbol sequences:

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.7275

Most of the other references I have found talk about quality relative to
gold standard judgments about whether exemplars are in the same class or
relative to similarity/distinctiveness ratios.  Neither is all that
satisfactory.

My preference is an entropic measure that describes how much of the
information in your data is captured by the clustering vs how much residual
info there is.

The other reference I am looking for may be in David Mackay's book.  The
idea is that you measure the quality of the approximation by looking at the
entropy in the cluster assignment relative to the residual required to
precisely specify the original data relative to the quantized value.

This is also related to trading off signal/noise in a vector quantizer.

David,  do you have a moment to talk about this with me?  I can't free up
the time to chase these final references and come up with a nice formula for
this.  I think you could do it in 10-20 minutes.

On Tue, Jul 14, 2009 at 6:41 AM, Grant Ingersoll <gsingers@apache.org>wrote:

> On Jun 17, 2009, at 2:51 AM, Ted Dunning wrote:
>
>  A principled approach to cluster evaluation is to measure how well the
>> cluster membership captures the structure of unseen data.  A natural
>> measure
>> for this is to measure how much of the entropy of the data is captured by
>> cluster membership.  For k-means and its natural L_2 metric, the natural
>> cluster quality metric is the squared distance from the nearest centroid
>> adjusted by the log_2 of the number of clusters.  This can be compared to
>> the squared magnitude of the original data or the squared deviation from
>> the
>> centroid for all of the data.  The idea is that you are changing the
>> representation of the data by allocating some of the bits in your original
>> representation to represent which cluster each point is in.  If those bits
>> aren't made up by the residue being small then your clustering is making a
>> bad trade-off.
>>
>> In the past, I have used other more heuristic measures as well.  One of
>> the
>> key characteristics that I would like to see out of a clustering is a
>> degree
>> of stability.  Thus, I look at the fractions of points that are assigned
>> to
>> each cluster or the distribution of distances from the cluster centroid.
>> These values should be relatively stable when applied to held-out data.
>>
>> For text, you can actually compute perplexity which measures how well
>> cluster membership predicts what words are used.  This is nice because you
>> don't have to worry about the entropy of real valued numbers.
>>
>
> Do you have any references on any of the above approaches?
>



-- 
Ted Dunning, CTO
DeepDyve

111 West Evelyn Ave. Ste. 202
Sunnyvale, CA 94086
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