Re: K-Means Clustering

[Duane Currie]

Hi folks,

Just a quick jump-in:

k-means needs a distance matrix, but it's k x n in size.

You can also partition the computation if you had extremely large n, but
in general, if n is very large, then you can typically uniformly subsample
(note: imho, the user should decide on if/how to subsample), and produce
approximately the same clustering results.

Duane

On Wed, Mar 9, 2011 at 9:21 AM, John Darrington
<address@hidden> wrote:
> Hi Mehmet!
>
> As I mentioned, the CLUSTER command is soemthing which I think it would be
> great to support.
>
> One issue with clustering is its memory complexity. It requires O(n^2) where
> n is the number of cases being clustered.
> Have you tested your algorithm with large numbers of cases?
>
> Maybe Ben has some ideas how an efficient distance matrix can be  implemented
> in PSPP (maybe sparse-array.c can help?) .
>
> In any case, I'd be interested to see your code, and the results of your
> comparisons.  Can you post them somewhere?
>
>
> J'
>
> On Tue, Mar 08, 2011 at 05:56:37AM -0800, Mehmet Hakan Satman wrote:
>     hi everybody,
>
>     I am interested in PSPP and i read about something about the needs for
>     developing some functionality.
>     I implemented a k-means clustering  library using the GNU scientific 
> library and
>     sent an informative e-mail to John. He suggested me to join this group 
> and share
>     my ideas with the stuff.
>
>
>     I compared the results with SPSS outputs. The analysis of variance table 
> is not
>     completed but we may add this feature.
>
>     I would be glad to integrate something to PSPP and work with you.
>
>     What do you think about this?
>
>
>
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