How KNIME decides what cluster values to use in k-means

I am an MSc student currently working on my master's thesis, which is due in 3 weeks, so this is kind of urgent ;)

I am using k-means to order my dataset in clusters. The dataset contains about 26,000 151-dimension vectors with values {0,1}. With such a high dimensional dataset, I am running k-means with maximum 500 iterations. 

What I can't figure out is how KNIME decides the best match in terms of which clusters it chooses. For each new run of k-means, the algorithm might come up with a new set of cluster centers. How does KNIME decide which cluster centers to use as the result?

Any reference to official documentation of any other help would be much appreciated.


Hi trbox:

Kmeans doesn´t choose cluster´s number. The analyst, in other words you, have to decide it. I recommend first run a Hierarchical cluster, but you must to sample because that technic doesn´t work well with 26,000 rows. Look dendogram.