A unifying model for biclustering
Iven Van Mechelen, Jan Schepers · 2006
A unifying biclustering model is presented for the simultaneous classification of the rows and columns of a rectangular data matrix. The model encompasses a broad range of (existing as well as to be developed) biclustering models as special cases, which all imply homogeneous data clusters on the basis of which the data can be reconstructed making use of a Sum- or Max-operator. An analysis of the objective or loss function associated with the model leads to two generic algorithmic strategies. In the discussion, we point at various possible model extensions. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.