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December 2005


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"Classification, clustering, and phylogeny estimation" <[log in to unmask]>
Alexander Rubchinsky <[log in to unmask]>
Sun, 11 Dec 2005 00:18:23 +0300
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"Classification, clustering, and phylogeny estimation" <[log in to unmask]>
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Dear Professor Healey:
I am not sure that I have understood your problem correctly. I present (for myself) a matrix,
whose rows correspond to objects (or items? is it the same?), columns correspond to words,
and element aij is equal to frequency of the j-th word in answers conserning i-th object.
If it is so and you want to classify objects, it is a well-known problem but it does not mean that
it is easy. The difficulty of the problem depends on the elements of the matrix.
If I understand the problem correctly, simply send me this matrix without any comments,
and I will use my advanced algorithms and inform you about results. You can obtain some 
impression about my methods from the new site 
 (see especially pattern matrix data format case, intended 
to analysis of such matrices (objects - properties).
If I have a wrong presentation of your situation, you can explain me the case in more detail.
I still think that it may be usefull.
Respectfully yours
Alexander Rubchinsky

-----Original Message-----
From: Michael Healy <[log in to unmask]>
Sent: Dec 10, 2005 4:53 AM
To: [log in to unmask]
Subject: classification of word association frequencies?

Hi Friends, I am looking for advice and recomendations about a data set that
i'd like to analyze where I will measure association by frequencies of

Let me explain in more detail. In my experiment participants were shown an
object and asked to choose words that described the item from a list of 50
words.  Responses were either 'yes' or no response was given.  This
procedure was repeated up to 3 times.

I now want to group the word-associates by target item, I have a rough idea
of what I want, but I'm open to suggestions from people who know more about
this than I do.  My plan is to find some method for finding the strength or
description of association between items by examining counts of numbers of
times items co-occured and produce a 'topographical map' where more
frequently listed items are closer together--by analogy, something like an
MDS model where we have a similarity matrix representing how often items are
co-occuring (or is that the answer?)

Anyway, looking forward to your input and suggestions, I think this is an
interesting although maybe standard problem.
Mike Healy