I'd be curious to see how far the naive approach would get you.  The high
dimensional nature of your problem makes exact k-NN less effective:
http://en.wikipedia.org/wiki/Curse_of_dimensionality

You could try using LSH (
http://en.wikipedia.org/wiki/Locality-sensitive_hashing) to cluster your
data, then use a use the hash for matching.  I've never used this before,
but it seems cool.

Here is an interesting SO question similar to your problem:
http://stackoverflow.com/questions/5751114/nearest-neighbors-in-high-dimensional-data

I'm probably not helping, but the problem is interesting to me :)

--Dan




On Sun, Apr 13, 2014 at 12:44 AM, Guyren Howe <[email protected]> wrote:

> On Apr 13, 2014, at 12:18 AM, Chris McCann <[email protected]> wrote:
>
> Thanks, Guy, that is the Cube patch I referenced in my reply earlier
> today.  At this point I'd rather not have to build a custom copy of
> Postgres to get this functionality, especially if I plan to deploy this app
> to a hosting provider like Heroku.
>
> That assumes I correctly understand what a “patch" is, so if I'm off the
> mark please enlighten me.
>
>
> That’s a standard contrib module, so it’s probably already there if you’re
> using a pretty standard build. There’s a good chance you can just do:
>
> create extension cube;
>
> Then do something like:
>
> select cube_union('(0,5,2),(2,3,1)', '0');
>
> Regards,
>
> Guyren G Howe
> Relevant Logic LLC
>
> guyren-at-relevantlogic.com ~ http://relevantlogic.com ~ +1 512 784 3178
>
> Ruby/Rails,  Xojo, PHP programming
> PostgreSQL, MySQL database design and consulting
> Technical writing and training
>
> Read my book, Real OOP with REALbasic: <
> http://relevantlogic.com/oop-book/about-the-oop-book.php>
>
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