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Table 3 Classification performance with real datasets

From: A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB

  TwoStep Latent Gold SNOB
Number of subgroups detected
MRI1 dataset 2 7 10
MRI2 dataset 3 11 15
MRI3 dataset 2 6 7
SMS dataset 2 10 37
Clinical dataset Not available 8 9
Certainty (mean classification probability of disc levels or patients)
MRI1 dataset Not available 91.2% (SD11.9%) 91.5% (11.6%)
MRI2 dataset Not available 98.9% (SD3.9%) 97.1% (SD6.6%)
MRI3 dataset Not available 85.7% (SD19.5%) 91.0% (SD12.7%)
SMS dataset Not available 96.5% (SD8.8%) 98.2% (SD4.7%)
Clinical dataset Not available 91.4% (SD12.9%) 89.9% (SD13.5%)
Reproducibility (10 iterations of each dataset, with identical results across all datasets)
Number of subgroups 100% agreement With fixed seed point = 100% agreement 100% agreement
Classification stability (reproducibility of individual disc-levels or people being classified into each subgroup) 100% agreement With fixed seed point = 100% agreement 100% agreement
Classification certainty (reproducibility of the classification probability of disc levels or patients) Not available With fixed seed point = 100% agreement 100% agreement