Consensus clustering: how to choose the final cluster based on the consensus matrix? I've been reading about consensus clustering and the consensus matrix in this article. I understand how the consensus matrix is made after re-sampling and clustering parts of your data H times. I understand that the consensus matrix is used to determine the optimal amount of clusters (k) and allows for making a nice heatmap. What I don't understand is how this consensus matrix results in your final clustering.
Say I have a consensus matrix of 4*4 (so we have 4 items to be clustered) where each value between 0 and 1 in the matrix represents the number of times items i and j are assigned to the same cluster, divided by the total numer of times both items are selected for clustering. We could have the following consensus matrix after 4 iterations of 80% sub-samples (taken from here). We kept track of all 4 clusters that were made in the process of obtaining this consensus matrix. How do we select the final clustering based on this consensus matrix?
Consensus clustering is an additional evaluation technique (i.e. how much consensus is there between clusters), like the Silhouette score, rather than explicit clustering.
The clusters themselves would drop out earlier in the processing. The consensus metric is used to test for the best K value, in the K-means clustering algorithm.