Research Article
A Novel Convex Clustering Method for High-Dimensional Data Using Semiproximal ADMM
Table 1
Results for Case I. Empirical mean and standard deviation (SD) of the RI, FMI, FNR, and FPR based on 50 repetitions.
| | Case I: | | Methods | RI | FMI | FNR | FPR | | Mean | SD | Mean | SD | Mean | SD | Mean | SD |
| | 2000 | K-means | 0.970 | 0.120 | 0.980 | 0.079 | 0.000 | 0.000 | 1.000 | 0.000 | | CC | 0.816 | 0.077 | 0.789 | 0.096 | 0.000 | 0.000 | 1.000 | 0.000 | | SCC | 1.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.223 | 0.010 | | SGLCC | 1.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| | 3000 | K-means | 0.980 | 0.099 | 0.986 | 0.069 | 0.000 | 0.000 | 1.000 | 0.000 | | CC | 0.804 | 0.076 | 0.773 | 0.096 | 0.000 | 0.000 | 1.000 | 0.000 | | SCC | 1.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.221 | 0.008 | | SGLCC | 1.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| | 4000 | K-means | 1.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | | CC | 0.784 | 0.083 | 0.747 | 0.104 | 0.000 | 0.000 | 1.000 | 0.000 | | SCC | 0.999 | 0.004 | 0.999 | 0.005 | 0.000 | 0.000 | 0.223 | 0.007 | | SGLCC | 1.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| | 5000 | K-means | 0.990 | 0.071 | 0.994 | 0.042 | 0.000 | 0.000 | 1.000 | 0.000 | | CC | 0.762 | 0.071 | 0.717 | 0.093 | 0.000 | 0.000 | 1.000 | 0.000 | | SCC | 0.998 | 0.006 | 0.998 | 0.006 | 0.000 | 0.000 | 0.224 | 0.008 | | SGLCC | 1.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.001 | 0.000 | 0.000 |
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