[R] finding a stable cluster for kmeans

[Wiebke Timm]

You might want to check if there is a neural gas algorithm in R.

kmeans generally has a high variance since it is very dependent on  
the initialization. Neural gas overcomes this problem by using a  
ranked list of neighbouring data points instead using data points  
directly. It is more stable (at the cost of additional computational  
time).

On 25.09.2007, at 05:00, Julia Kröpfl wrote:

> I applied kmeans to my data:
>
> kcluster= kmeans((mydata, 4, iter.max=10)
> table(code, kcluster$cluster)
>
> If I run this code again, I get a different result as with the  
> first trial (I understand that this is correct, since kmeans starts  
> randomly with assigning the clusters and therefore the outcomes can  
> be different)
> But is there a way to stabilize the cluster (meaning finding the  
> one cluster that appears the most often in 10 trials)?
>
> Thank you for any ideas,
> Julia
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