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Table 5 Comparison of the clustering results for the Pomeroy dataset

From: Clustering gene expression data using a diffraction‐inspired framework

Algorithm AEC AVI ACC (%)
Diffractive clustering 63.1 258.7 67.8
k‐means 42.7 173.6 48.9
Fuzzy c‐means 39.9 154.6 43.3
Hierarchical clustering (single) 49.8 256.2 56.7
Hierarchical clustering (centroid) 50.7 292.6 57.8
Self‐organising map 41.2 13.7 44.4
Gaussian Mixture Model 42.2 79.2 37.7
DBSCAN 36.6 46.4 33.3
  1. The diffractive clustering algorithm outperforms the others in terms of accuracy, with the density‐based and model‐based clustering algorithms performing relatively poorly due to the excessive mixing between the five different classes.