[vtkusers] K-means values

[Sara Rolfe]

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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