MCAM is the ensemble clustering method for unsupervised learning published here. Clustering is a method that seeks to group multidimensional data such that similar objects are in a group and in different group than dissimilar objects. However, since this definition changes in vector space if you alter the 1) distance metric used, 2) the transformation used, 3) the algorithm used, or 4) the number of clusters sought, then all solutions are hypotheses about the structure of the underlying data and there are a myriad of possible hypotheses for the same data.  Therefore, in ensemble clustering, we take into account many (thousands sometimes) possible solutions.  This code was written for ensemble clustering in Matlab.