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Table 3 Comparison of biological coherence of clusters obtained for dataset II by different clustering algorithms

From: Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures

  Proportion of Genes (%) in Clusters of p-values
  <= 10-4 <= 10-5 <= 10-4 <= 10-5
  (Uncorrected) (Bonferroni-Corrected)
EP_GOS_Clust 79.4* 66.1* 57.2 52.2*
Iterated EP_GOS_Clust 86.2* 83.9* 73.1* 68.9*
K-Means 78.0 62.1 58.0* 51.7
K-Correlation 77.1 63.9 57.3* 51.8
K-Medians 78.8* 65.3 56.9 52.2*
SOTA 75.2 66.9* 57.1 39.3
IClust 66.0 54.0 34.2 29.1
  Cluster Correlation -log10(P) Values
  Max. Min. Ave. Average
EP_GOS_Clust 0.920 0.454* 0.730* 9.17*
Iterated EP_GOS_Clust 0.956* 0.489* 0.750* 11.09*
K-Means 0.961* 0.049 0.668 9.01
K-Correlation 0.964* 0.398* 0.717* 9.13
K-Medians 0.923 0.203 0.683 9.09
SOTA 0.911 0.285 0.624 9.20*
IClust N.A. N.A. N.A. 9.01
  1. Methods include EP_GOS_Clust backbone, the iterative algorithm described in this report, the K-family of partitional clustering algorithms with pre-assigned clusters, self organizing tree algorithm (SOTA), and mutual information based clustering (IClust) [28]. Data in the upper table are presented as described in the legend to Table 2 while the lower table presents data on expression correlation within clusters and the average -log(P) values for biological coherence over all the clusters.
  2. * The top three performers in each category are indicated with an asterisk.