Benchmarking different clustering methods on simulated data.
JointCluster detected implanted clusters on instances of randomly generated graphs
better than Coassociation and single tree methods, especially when the cluster structure was not strong, in two cases: (A) noise level in both
were varied together, (B) noise level
was fixed at
was varied from 0 to 16. The quality of the clustering detected by a method is measured as the standard Jaccard index measure between the detected and true clustering (y axis), averaged over all random instances for each setting of the noise level parameter
(x axis). The average number of edges incident at each node is 16, so
indicates a false positive rate of 50%.
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