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From Alexander Kerner <a.ker...@dkfz-heidelberg.de>
Subject Re: Clustering Data
Date Tue, 16 Aug 2011 08:47:15 GMT
Hello Ted,

thanks for your help!

To give you more details:
Clustering in this case has something of pattern recognition:

for the first graph, I am looking for following pattern:

      *   *
    *          *
*                   *

for the second graph, I basically want following "pattern":

*  *  *  *  *  *

What I want to detect is now "overlaying" data or at a very basic point 
of view just changes from expected pattern:

e.g:

first case:


     *  *       *
   *       *  *   *
*

should be clustered into two groups

second case:

*  *  *
            *  *  *

should also clustered into two groups.

In general, I am working with very little datapoints ( 5 - 50).

I hope this makes it a bit more clear.

May thanks,
Alex

On 08/15/2011 05:08 PM, Ted Dunning wrote:
> Well, weka still stands as an option.  And frankly, you can call R from java
> pretty easily.
>
> But more importantly, *experimenting* with these alternatives doesn't need
> to be in Java.  You can noodle around with all the clustering algorithms in
> the world, select one and port it into Java or find an implementation.
>
> And if you don't describe your problem in a bit more detail, we can't help
> you.  Clustering specifically and machine learning in general is domain
> dependent.
>
> Your graphs don't explain what your data is, what your are trying to do,
> what results you expect to get nor why you don't like the results you are
> getting.  It is not obvious.
>
> I note that I asked essentially these same questions 10 days ago.
>
> On Sun, Aug 14, 2011 at 11:35 PM, Alexander Kerner<
> a.kerner@dkfz-heidelberg.de>  wrote:
>
>> Matlab or R is not an option, since I need to integrate this clustering
>> into an existing Java program.
>>
>> On 08/05/2011 06:02 PM, Jeff Eastman wrote:
>>
>>> You may be better off experimenting with Weka (or MatLab or R) to try out
>>> various clustering algorithms on your data. Unless you have billions of
>>> points this sort of low-dimension clustering can all be done in memory and
>>> you don't need Mahout.
>>>
>>>
>>> -----Original Message-----
>>> From: Alexander Kerner [mailto:a.kerner@dkfz-**heidelberg.de<a.kerner@dkfz-heidelberg.de>
>>> ]
>>> Sent: Friday, August 05, 2011 7:28 AM
>>> To: user@mahout.apache.org>>   "user@mahout.apache.org"
>>> Subject: Re: Clustering Data
>>>
>>> Here is a link:
>>>
>>> Clustering data<http://kerner.cc/box.**tightening.challenges.png<http://kerner.cc/box.tightening.challenges.png>
>>> On 08/05/2011 02:31 PM, Sean Owen wrote:
>>>
>>>> (Attachments don't come through on apache.org<http://apache.org>
>>>> mailing lists. Can you post it elsewhere, or describe it?)
>>>>
>>>> On Fri, Aug 5, 2011 at 1:30 PM, Alexander Kerner
>>>> <a.kerner@dkfz-heidelberg.de<**mailto:a.kerner@dkfz-**heidelberg.de<a.kerner@dkfz-heidelberg.de>>>
>>>>   wrote:
>>>>
>>>>      Hi all,
>>>>
>>>>      I would like to cluster following data (see attached picture) into
>>>>      three
>>>>      groups (light blue, dark blue, black).
>>>>      Can I use Apache Mahout for this? I want to integrate clustering
>>>>      within
>>>>      my existing Java application.
>>>>      What algorithm would I need to use and how do I set this up
>>>>      programatically?
>>>>
>>>>      Many thanks,
>>>>      Alex
>>>>
>>>>
>>>>
>>>>
>> --
>> Alexander Kerner
>> PhD Student
>>
>> Divison of Stem Cells and Cancer A010
>> German Cancer Research Center, DKFZ
>> and
>> Heidelberg Institute for Stem Cell Technology
>> and Experimental Medicine
>> HI-STEM GmbH
>>
>> Neuenheimer Feld 280
>> 69120 Heidelberg
>>
>> Tel.: +49(0)6221/42-3922
>> Fax: +49(0)6221/42-3902
>>
>> Email: A.Kerner@dkfz-heidelberg.de
>>
>>

-- 
Alexander Kerner
PhD Student

Divison of Stem Cells and Cancer A010
German Cancer Research Center, DKFZ
and
Heidelberg Institute for Stem Cell Technology
and Experimental Medicine
HI-STEM GmbH

Neuenheimer Feld 280
69120 Heidelberg

Tel.: +49(0)6221/42-3922
Fax: +49(0)6221/42-3902

Email: A.Kerner@dkfz-heidelberg.de


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