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Table 3 Validation Results: The mean classification error, normalized mutual information (NMI) and stability, on all datasets, are shown, measuring the agreement between the clusters resulting from an approach and the real patient classification

From: MVDA: a multi-view genomic data integration methodology

  Feature Integration Algorithm Error NMI Stability
Single View All Feature - Ward 30,08 % 26 % 86 %
   - Kmeans 30,93 % 25 % 51 %
   - Pamk 30,75 % 24 % 94 %
  Selected Prototype - Ward 30,72 % 26 % 89 %
   - Kmeans 30,36 % 25 % 52 %
   - Pamk 30,78 % 24 % 96 %
Multi-View All Feature Early Integration Tw-kmeans 37,10 % 24 % 69 %
  All Feature Intermediate Integration SNF 30,83 % 22 % 83 %
  All Feature in Cluster of Selected Prototype Intermediate Integration SNF 31,31 % 18 % 82 %
  Selected Prototype Late Integration unsupervised MF/GLI 27,47 % 28 % 85 %
  Selected Prototype Late Integration semi-supervised MF/GLI 6,30 % 63 % 84 %
  1. Bold font in percentage indicates best performance in the experiments