MCAM is the application of unsupervised learning to high throughput biological datasets of quantitative measurements. Specifically, since the perturbation of data by transformations or changes in the algorithm or distance metric used affects the resulting clustering solution, MCAM seeks to combine large numbers of clustering solutions to better understand the solution space of resulting clusters.
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MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets.
Naegle KM, Welsch RE, Yaffe MB, White FM, Lauffenburger DA.
PLoS Comput Biol. 2011 Jul;7(7):e1002119. doi: 10.1371/journal.pcbi.1002119.