How to get identical output from R apcluster and Sandia Cognitive Foundry AffinityPropagation

[Hitesh Bagchi]

I am migrating an R script to Java. The R script uses the apcluster library. I am trying to recreate the same output using the Sandia Cognitive Foundry AffinityPropagation class. But I am finding it difficult to tune the selfDivergence value appropriately.

Here is my R and Java code.

library(apcluster)

NgramAdjMatrix <- matrix(
  c(0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 0.0, 
    2.0, 4.0, 0.0, 3.0, 6.0, 0.0, 4.0, 8.0, 0.0, 5.0, 10.0, 0.0, 6.0, 12.0), 
  nrow=7, 
  ncol=3, 
  byrow = T)

LatentClusters <- apcluster(negDistMat(r=2),NgramAdjMatrix,seed=1234)
representatives <- LatentClusters@exemplars
clustMembers <- LatentClusters@clusters
FinalNgramMatrix <- NgramAdjMatrix[representatives,]

Above R scripts gives this output,

    [,1] [,2] [,3]
[1,]   0    1    2
[2,]   0    4    8

Here is my Java code using Cognitive Foundry

Vector[] data = new Vector[]{
        new Vector3(0.0, 0.0, 0.0),
        new Vector3(0.0, 1.0, 2.0),
        new Vector3(0.0, 2.0, 4.0),
        new Vector3(0.0, 3.0, 6.0),
        new Vector3(0.0, 4.0, 8.0),
        new Vector3(0.0, 5.0, 10.0),
        new Vector3(0.0, 6.0, 12.0)
    };

    System.out.println(Arrays.toString(data));

    AffinityPropagation<Vectorizable> instance
            = new AffinityPropagation<>(
                    EuclideanDistanceSquaredMetric.INSTANCE, 6);
    Collection<CentroidCluster<Vectorizable>> clusters = instance.learn(Arrays.asList(data));

    clusters.stream().forEach((cluster) -> {
        System.out.println(cluster.getCentroid() + "...");
    });

Above Java code gives this output,

<0.0, 1.0,  2.0>
<0.0, 2.0,  4.0>
<0.0, 5.0, 10.0>

The output is different and dependent to a very large extent on the selfDivergence parameter which is set to 6 in my code.

Is there some way to make the Java code behave same as the R code?

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P.S: I have also posted this question to SO earlier