[vtkusers] K-means values

[Sara Rolfe]

It seems like I could solve this by using learning and specifying one  
iteration, but this seems awkward.  If anyone is aware of a better way  
to access the means from the kMeansStatistics output, could you let me  
know?

Thanks,
Sara

On Mar 15, 2011, at 3:51 PM, Sara Rolfe wrote:

> Hi David,
>
> Thanks for your reply.  Right now I'm using vtkKmeansStatistics  
> without learning and am following the example here:
>
> http://www.vtk.org/Wiki/VTK/Examples/InfoVis/KMeansClustering
>
> The output that I get using kMeansStatistics->GetOutput()->Dump()  
> shows the original value, the distance to the nearest cluster, and  
> cluster id it is assigned to, instead of the cluster mean.
>
> +-----------------+-----------------+------------------+
> | Magnitude       | distance (0)    | closest id (0)   |
> +-----------------+-----------------+------------------+
> | 0.0657005       | 6.44972e-06     | 4                |
> | 0.0652216       | 4.24651e-06     | 4                |
> | 0.0646891       | 2.33557e-06     | 4                |
> | 0.0641142       | 9.08931e-07     | 4                |
> | 0.0635069       | 1.19747e-07     | 4                |
> | 0.0666587       | 1.2235e-05       | 4                |
>
> I think I will probably use learning, but I'd like to get it working  
> without first.
>
> Thanks,
> Sara
>
> On Mar 15, 2011, at 3:27 PM, Thompson, David C wrote:
>
>> Hi Sara,
>>
>>> I'm using vtkKmeansStatistics to successfully cluster data points.
>>> However, I'm missing how you access the actual cluster mean values,
>>> instead of just their labels.  It looks like the order of the labels
>>> may not correspond to the values of the means, is this true?
>>
>> I'm not clear on what you mean by "label". I've run the filter on  
>> data with 2 columns (named x & y) and with 2 sets of initial  
>> cluster center coordinates specified on the LEARN_PARAMETERS input:  
>> one for k=2 and one for k=3. I get this table:
>>
>> +----------------+----------------+----------------+---------------- 
>> +----------------+----------------+-----------------+
>> | Run ID         | k              | Iterations     | Error           
>> | Cardinality    | x              | y               |
>> +----------------+----------------+----------------+---------------- 
>> +----------------+----------------+-----------------+
>> | 0              | 2              | 3              | 1528.94         
>> | 772            | 0.166201       | 0.12059         |
>> | 0              | 2              | 3              | 498.266         
>> | 228            | 2.79467        | 2.99856         |
>> | 1              | 3              | 15             | 546.596         
>> | 397            | -0.341883      | -0.486857       |
>> | 1              | 3              | 15             | 546.946         
>> | 405            | 0.758854       | 0.855424        |
>> | 1              | 3              | 15             | 381.077         
>> | 198            | 2.99941        | 3.14951         |
>> +----------------+----------------+----------------+---------------- 
>> +----------------+----------------+-----------------+
>>
>> as the first block of output 1 (i.e.,  
>> GetOutputDataObject( 1 ).GetBlock( 0 ).Dump() will produce the  
>> above). The first 2 rows contain the cluster mean values  
>> corresponding to the run with k=2 and the final 3 rows have the  
>> same for the run with k=3. Because there are 2 coordinates (x & y)  
>> for each cluster center, there is no good way to order cluster  
>> centers by their means. Instead, their order matches the initial  
>> guesses at cluster centers specified on the LEARN_PARAMETERS input  
>> if it exists. Otherwise, the order is random because the initial  
>> guesses are produced randomly. Is this what you wanted to know?
>>
>>    David
>
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