Figure 1
From: Merged consensus clustering to assess and improve class discovery with microarray data
Calculating the consensus clustering result. The results of any discrete clustering algorithm can be represented as a membership list in which the features are indexed by cluster. (A) The clustering result can be readily converted into a connectivity matrix representing the co-clustering connections of the features. In a consensus clustering experiment the clustering process is performed many times with sub-samples of the data rows and the resulting partial connectivity matrices are summed. In addition, the frequency with which pairs of features are drawn together are counted and summed to produce an indicator matrix quantifying the opportunity any two members have to cluster together. (B) By dividing the connectivity and indicator matrices we produce the final consensus matrix which measures the frequency with which any two features cluster together.