Data from multiple techniques can be combined into a composite data set and therefore into one composite clustering. Similarities can be adopted from the individual experiments and averaged using different weighting strategies. Alternatively, all characters from the individual experiments can be pooled to form one global data set, which can be clustered. Using a mathematical linearization model, a consensus similarity matrix and dendrogram can be calculated based upon individual matrices from different experiments.
Composite data sets allow transversal clustering, in which characters and entries are simultaneously clustered based upon the swapped data matrix.