Are you just looking for metrics related to the partition you've created, or are you wanting to redistribute some other data according to the partition?
Eda Oktay <eda.ok...@metu.edu.tr> writes: > Hello everyone, > > I am solving a graph partitioning problem. I found an unnormalized > Laplacian matrix of a graph, then since I have 4 processes and I am > using spectral partitioning algorithm, I calculated 4 eigenvectors > corresponding to 4 smallest eigenvalues of this Laplacian matrix. > > Then, since I am using the k-means clustering algorithm, I formed a > matrix U whose columns are these eigenvectors. After that, I clustered > each row of U according to the k-means algorithm. Now, I have > different row vectors at different processes, as I want. > > In other words, my eigenvectors have 72 elements. So, right now, I > have 72 different row vectors with 4 elements. Those 72 row vectors > were clustered but not in a serial order. For instance, the 4th vector > is in the first process and the 5th one is in the 4th process. > > As the last part of my work, I want to find mincut of this > partitioning but I couldn't understand how to do it. If I didn't use > k-means algorithm, I would use MatPartitioningApply but now, since I > clustered row vectors of U according to the index set I obtained from > k-means algorithm, I don't know how to use partitioning routines. > Should I form another matrix by reordering these vectors and then > partition? But then, how can I be sure the vectors stay in the same > process as before? Besides MatPartitioning routines, is there any > routine I can use for this? I thought MatColoring can work but I guess > it won't. > > Thanks a lot! > > Eda
