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Table 3 The average NMIs and ACCs and standard errors obtained by the ECMC and other comparison partners on real benchmark datasets

From: Subtype identification from heterogeneous TCGA datasets on a genomic scale by multi-view clustering with enhanced consensus

  Methods Cora Texas Wisconsin Washington Cornell
NMI SV1 0.021 ±0.001 0.175 ±0.001 0.273 ±0.004 0.252 ±0.001 0.182 ±0.002
  SV2 0.004 ±0.000 0.098 ±0.001 0.064 ±0.001 0.096 ±0.002 0.083 ±0.001
  Concat 0.002 ±0.000 0.120 ±0.001 0.120 ±0.001 0.128 ±0.001 0.156 ±0.001
  Coreg 0.025 ±0.001 0.234 ±0.002 0.284 ±0.005 0.306 ±0.002 0.213 ±0.005
  SNF 0.013 ±0.000 0.156 ±0.003 0.303 ±0.001 0.204 ±0.006 0.200 ±0.001
  CMC 0.085 ±0.003 0.316 ±0.002 0.343 ±0.003 0.328 ±0.002 0.326 ±0.002
  ECMC 0.688 ±0.000 0.348 ±0.002 0.419 ±0.003 0.380 ±0.001 0.343 ±0.005
ACC SV1 0.587 ±0.003 0.570 ±0.001 0.533 ±0.004 0.440 ±0.001 0.456 ±0.004
  SV2 0.544 ±0.000 0.563 ±0.001 0.462 ±0.002 0.490 ±0.001 0.453 ±0.001
  Concat 0.511 ±0.001 0.383 ±0.003 0.375 ±0.003 0.375 ±0.002 0.411 ±0.001
  Coreg 0.590 ±0.001 0.612 ±0.001 0.558 ±0.004 0.519 ±0.003 0.496 ±0.005
  SNF 0.549 ±0.000 0.601 ±0.000 0.587 ±0.003 0.551 ±0.006 0.497 ±0.000
  CMC 0.665 ±0.004 0.468 ±0.003 0.578 ±0.005 0.492 ±0.002 0.479 ±0.002
  ECMC 0.935 ±0.000 0.566 ±0.001 0.635 ±0.001 0.648 ±0.002 0.539 ±0.002
  1. The highest NMI and ACCs are marked in bold