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.
-trbox