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Fast tree aggregation for consensus hierarchical clustering

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Academic year: 2021

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Figure

Figure 1 and Table 1 provide a toy example to illustrate how the method works. The third hierarchical  cluster-ing is the result of the mergcluster-ing of the first two
Fig. 2 Timing simulations for the different methods. Number of tables, individuals and features are set to 3, 100 and 100, respectively, when they are not the variable of interest
Fig. 3 Simulation results for different separability factors and two scenarios. Scenario 1 (top row): each informative vector contains information on the 5 groups
Table 2 NID values and number of groups, results for the cell-type data set, taking 3 axes for the spectral decomposition
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