Skip to main content

Table 3 The clustering results of all methods on the different integrated datasets

From: Multi-cancer samples clustering via graph regularized low-rank representation method under sparse and symmetric constraints

DatasetsMeasureMethod
K-meansT-SNELLENMFPCALRRLLRRMLLRRsgLRR
CO-CHACC95.4089.3171.1493.1493.6397.9999.6698.9998.66
MCC80.0670.2188.5875.5372.9946.7691.0690.7895.20
RI91.4349.8563.6687.1888.0796.0499.3298.0097.34
NMI66.4651.0175.7154.1254.4741.9280.5177.0487.51
PA-ESACC98.2591.8477.2699.1699.1696.3899.1699.1699.16
MCC84.7383.7462.0498.3498.3477.9896.7196.7198.34
RI97.3784.9766.4698.3598.3393.0098.3498.3498.34
NMI81.0659.4941.4393.8693.8665.4389.3989.3993.86
CH-HN-COACC89.2283.0380.9976.9985.7795.8697.9984.7798.28
MCC67.9165.6960.7782.2569.1761.2368.3180.9687.25
RI90.0087.1682.8580.1987.7694.7096.6784.6697.48
NMI73.5578.4369.0776.2272.5968.2573.8177.8380.99
ES-CH-HNACC85.5652.3561.6584.5280.0382.1793.1994.3296.11
MCC66.2632.6242.0167.4466.0843.0461.1866.4991.32
RI82.6760.9763.8980.2578.1572.3689.0790.3093.10
NMI56.7730.7733.2647.3572.5936.9252.3857.2078.75
CO-CH-ES-HNACC86.8960.5263.0482.3181.3279.2492.4887.9494.17
MCC70.0065.3079.3051.7871.3052.4478.0573.0391.71
RI89.4374.5971.9885.0786.5781.5891.4288.9593.34
NMI71.0448.4352.0057.6769.1254.7674.4269.8280.27
ES-CO-PA-HNACC86.8985.8367.7682.3181.3279.2492.4887.9494.17
MCC79.5178.5277.4084.2389.8259.2188.3883.6385.49
RI89.4391.4578.0685.0786.5781.5891.4288.9593.34
NMI76.1581.4255.9372.9779.5361.8281.2476.5876.23
  1. Note: The best clustering results are highlighted in bold