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From: kerog@sp.isl.secom.co (Keith Rogers)
Subject: Re: K-Means distribution
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In article <CzFMM5.8q3@unx.sas.com>, saswss@hotellng.unx.sas.com (Warren
Sarle) wrote:

> There are many variations on k-means, of which the one below is one of
> the simplest. It works quite well for many applications except for the
> initialization method.
> 
> In article <CzF8FB.7M0@acsu.buffalo.edu>, jn@cs.Buffalo.EDU (Jai
Natarajan) writes:
> |>
> |> K-Means :
> |>
> |> Lets say you have n nodes or centres
> |> Initialise them to random values
> 
> This type of initialization is prone to degenerate solutions--one or
> more clusters are likely to have zero members. It is better to choose a
> subset of the training cases as initial centers. This can be done
> randomly or systematically. The systematic algorithm used in the
> FASTCLUS procedure in the SAS/STAT product is guaranteed to produce a
> global optimum if the data contain well-separated clusters. For RBF
> applications, however, one would not often expect to have well-separated
> clusters.

Thanks for the info, but could you please add a little more there?
What does one do instead for RBF applications?
