This operator clusters a set of tuples.


  • ATTRIBUTES: The attributes of the incoming tuples that should be recognized for clustering by the distance/similarity function. Notice, not all kinds of attribute types work here
  • learner: The clustering algorithm that should be used
    • Currently implemented: kMeans, Weka (which in turn has further algorithms)
  • ALGORITHM: A set of options to describe the algorithm


This example uses the weka-clusterer. The weka-clusterer should use the "simplekmeans" algorithm. the arguments to set up the weka-simplekmeans is "-N 3".

clustered = CLUSTERING({
               algorithm ='SimpleKMeans',
               options = [
               		['arguments','-N 3 -I 500 -S 10 -O']]                                      
                }, input)

For weka, there are currently the following algorithms that can be used as the "model". Further details and possible arguments can be  found in the Weka Docs

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