Standard standardising would be subtract mean and divide by standard deviation. i.e. it would be clustering by mahalanobis distance.


Kind regards,-- 
Dominykas Grigonis


On Wednesday, 23 April 2014 at 15:53, Adam Ginensky wrote:

> I'm looking at clustering of stocks based on their fundamental financial
> data. I have about 80 variables per stock. I have the standard k-means
> package. Firstly, I am wondering if there are any other R packages that
> may be more useful for clustering of financial data.
> My second, and more important (to me), question is- Should one scale the
> data before clustering. I'm particularly worried that since certain
> variables can be orders of magnitude larger than other equally interesting
> variables (-think market cap and p/e). I realize this is not an R question
> per se, but I feel I am more likely to get a good answer out of this forum
> than any other because of the concentration of financial practitioners. Of
> course, I apologize in advance, if it is too 'off-topic' and then simply
> ask for a better place to post. Thanks.
> 
> Adam Ginensky
> 
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> 
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> 
> 



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