[Java-ML-support] Read MCL result from SparseMatrix representation

Dear all,


I am investigating java-ml for its Markov CLustering implementation.
In my current project, I have a in-memory model of 2D adjency matrix (a two-dimensional array of the double primitive type : double[][]).

Note that this matrix is sparse in the sense that it is triangular superior (I am using a symetric distance, hence I don't fill the inferior triangle of my 2D matrix).

Fortunately, I can convert it into a SparseMatrix using the associated constructor and then run the MCL algorithm.

The code fragment from my program is as follows.

double[][] adjencyMatrix;

// omitted operations to fill adjencyMatrix

SparseMatrix smatrix = new SparseMatrix(adjencyMatrix);
MarkovClustering mcl = new MarkovClustering();
double maxResidual = 0.001;
double gammaExp = 2.0;
double loopGain = 0.;
double zeroMax = 0.001;
SparseMatrix matrix = mcl.run(smatrix, maxResidual, gammaExp, loopGain, zeroMax);

From this point, I understand that "matrix" helds a "sparse matrix with row-major format" of my results, that is, a compressed version of the traditional 2D matrix resulting from MCL. I am able to read the MCL results from a traditional 2D data structure, yet I don't know to grasp how to unveil clusters from the resulting SparseMatrix.

I have been unable to find the proper documentation to understand the SparseMatrix data structure used in java-ml. Can you point me to the paper or documentation that you may have used to create the data structure ? If no, is it possible to convert a SparseMatrix into a double[][] (which I suspect to be performance killing) ?

Thanks you in advance for any help !

Best Regards,

Chris