This operator clusters a set of tuples.

Parameter

• 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

Example

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({
               attributes=['source'],
               learner='weka',               
               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

• SIMPLEKMEANS (http://weka.sourceforge.net/doc.dev/weka/clusterers/SimpleKMeans.html)
• EM (http://weka.sourceforge.net/doc.dev/weka/clusterers/EM.html)
• COBWEB (http://weka.sourceforge.net/doc.dev/weka/clusterers/Cobweb.html)
• FARTHESTFIRST (http://weka.sourceforge.net/doc.dev/weka/clusterers/FarthestFirst.html)
• DENSITY_KMEANS (http://weka.sourceforge.net/doc.dev/weka/clusterers/MakeDensityBasedClusterer.html)
• HIERARCHICAL (http://weka.sourceforge.net/doc.dev/weka/clusterers/HierarchicalClusterer.html)