For posterity - someone pointed me to -seqcomp-, which was exactly was
I was looking for.
On Fri, May 1, 2009 at 12:17 PM, Dan Weitzenfeld
<[email protected]> wrote:
> Hi Folks,
> In working with eye-tracking data, a person's sequence of
> areas-of-interest viewed (a "scan pattern") are often represented as
> strings. E.g., my scan-pattern in the first 5 seconds of looking at a
> webpage might be PPHM, with P = picture, H = headline, and M = side
> menu.
> For this reason, I am interested in clustering on string similarity,
> to identify commonly-taken scan patterns in a dataset.
> It looks like my best bet is to create a dissimilarity matrix (using
> Levenshtein distance has the dissimilarity measure) and then use
> -clustermat-.
>
> My questions are:
> -are there any packages out there that would make this easier?
> -am I right that I will have to write a program to make the matrix?
> I'm fine with writing it, I just want to confirm that I'm not missing
> an easier way to do this.
>
> Thanks in advance,
> Dan
>
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>
--
Dan Weitzenfeld
Media Analyst
EmSense Corporation
512 2nd Street, 3rd Floor
San Francisco, CA 94107
w: 415.418.7314
m: 510.552.0106
[email protected]
*
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