java weka.clusterers.MPCKMeans \
	-D data/iris.arff \
	-C data/iris.constraints \
	-K 4 \
	-T 4 \
	-M weka.core.metrics.WeightedEuclidean \
	-L weka.clusterers.metriclearners.WEuclideanLearner \
	-G weka.clusterers.regularizers.Rayleigh \
	-A weka.clusterers.assigners.SimpleAssigner \
	-I weka.clusterers.initializers.WeightedFFNeighborhoodInit \
	-N 3 \
	-R 42 \
	-l 0.01 \
	-r 0.001 \
	-m 1 \
	-c 1 \
	-i 200 \
	-B 20 \
	-O iris.assignments \

Notes: 

1. Only change the -D, -C, -N, -O, -K options for different datasets,
can leave everything else as specified in the options above. 

2. If -V specified, *no* transitive closure is taken for the constraints.

3. MPCK-Means is constrained by default. Use -X only if you want to turn 
constraint satisfaction off during the E-step.

4. No need to give -N option if -K is specified.