You can use random forests and k-medoids algorithms or search for term
multiobjective clustering in google.


On Wed, Apr 23, 2014 at 6:41 PM, aschmid1 <aschm...@stevens.edu> wrote:

> One way to deal with variables of different nature/units is to scale them
> with their standard deviations.
>
>
> On 04/23/2014 10:53 AM, 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
>>
>>         [[alternative HTML version deleted]]
>>
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>
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