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Figure 4 | BMC Bioinformatics

Figure 4

From: Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

Figure 4

Performance on toy models. We validated our algorithm on graphs with predefined cluster structure. To this end, we compared it with the hard clustering method by Long et al. on four different random toy models, see Table 1. The plot shows the mean relative deviation between the two algorithms relative to the results of the hard clustering. Error bars denote standard deviations over 1000 runs. We see that the fuzzy cluster assignments of our method require much more runtime, but both cost function and data estimation error (see Methods) are significantly smaller. The large standard deviations show the dependency of the decomposition on the random initial conditions. Therefore, by default we perform multiple restarts with different initializations.

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