Interesting. I found that if I take the 4th power of the y variable, I
get a few decent clusters. I was planning on doing a linear fit of
those good clusters, and then using that to find the trailing data. Add
in a small histogram to find the lower x bound of what I want and it
should work. Your idea also sounds promissing. I'm getting close.
Hopefully, I'll have something that'll work by Tuesday, and I'll let
you all tear it to pieces...err find more efficient ways of doing it.
=)
Thanks,
- Rob
JD Smith wrote:
> On Fri, 11 Aug 2006 10:22:35 -0700, kuyper wrote:
>
>> rdellsy@gmail.com wrote:
>>> I'm working on doing a cluster tree and getting say the lower-right
>>> cluster and the one or two nearest neighbors (sp?). I may still be
>>> loosing some data though. Another possibilty would be compressing the
>>> data, say, by half, and see if that helps.
>>> Thanks,
>>> Rob
>>
>> IDL> help,data
>> DATA FLOAT = Array[2, 681]
>>
>> If all of the dimensions of your data have the same physical meaning,
>> then
>> you don't need to do anything to your data. However, I got the
>> following
>> results:
>>
>> IDL> print,stddev(data[0,*]),stddev(data[1,*])
>> 2748.5689 1.7135388
>>
>> Which implies to me that your x and y coordinates probably have
>> drastically
>> different meanings, so they need to be scaled to have a meaningful
>> distance
>> measurement. The simplest way is to base the scale factors on the
>> standard deviations:
>>
>> IDL> scaled = data
>> IDL> scaled[0,*] /= stddev(data[0,*])
>> IDL> scaled[1,*] /= stddev(data[1,*])
>>
>> I recommend, since you're analyzing many different but comparable
>> datasets, to use a single scaling factor on each axis for all the
>> datasets; otherwise it will be difficult to compare your results
>> between one dataset and another.
>>
>> IDL> pairdistance = DISTANCE_MEASURE(scaled)
>> IDL> clusters =
>> CLUSTER_TREE(pairdistance,linkdistance,LINKAGE=0,data=scaled )
>>
>> I'm surprised by the fact that I haven't been able to locate an IDL
>> function or procedure for taking the output from CLUSTER_TREE and using
>> it to determine cluster membership at the point
>> when there are N clusters left, so I wrote my own:
>>
>> FUNCTION cluster_member, clusters
>> dims = SIZE(clusters, /DIMENSIONS)
>> num = dims[1] + 1
>> membership = INTARR(num, num-1)
>> work = indgen(num)
>> FOR i=0, num-2 DO BEGIN
>> newclust = WHERE (work eq clusters[0,i] OR work EQ
>> clusters[1,i])
>> work[newclust] = num+i
>> membership[0,i] = work
>> ENDFOR
>>
>> RETURN, membership
>> END
>>
>> There's probably a more efficient way of handling that loop.
>
> Very cool! I'll have to remember this one. If you only care about n
> remaining clusters, you can simplify somewhat to:
>
> function cluster_member, clusters,n
> dims = SIZE(clusters, /DIMENSIONS)
> num = dims[1] + 1L
> n>=1
> work = lindgen(num)
> for i=0L, num-1L-n do $
> work[where(work eq clusters[0,i] OR work eq clusters[1,i])]= num+i
> return, work
> end
>
> JD
|
comp.lang.idl-pvwave archive
Members
Search
Help
Login
Home
