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• 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
• CLUSTERERlearner: 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

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clustered = CLUSTERING({
                attributes=['agesource', 'income'],
                learner='weka',                
                algorithm =                'SimpleKMeans',
               options =  [
                  'model'='SimplekMeans'
                  , 'arguments'=		['arguments','-N 3'
 -I 500 -S 10  -O']]            ]                                 
  
              }, inputoperatorinput)

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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