  Being an unsupervised machine learning and data mining technique,
biclustering and its multimodal extensions are becoming popular tools for
analysing object-attribute data in different domains. Apart from conventional
clustering techniques, biclustering is searching for homogeneous groups of
objects while keeping their common description, e.g., in binary setting, their
shared attributes. In bioinformatics, biclustering is used to find genes, which
are active in a subset of situations, thus being candidates for biomarkers.
However, the authors of those biclustering techniques that are popular in gene
expression analysis, may overlook the existing methods. For instance, BiMax
algorithm is aimed at finding biclusters, which are well-known for decades as
formal concepts. Moreover, even if bioinformatics classify the biclustering
methods according to reasonable domain-driven criteria, their classification
taxonomies may be different from survey to survey and not full as well. So, in
this paper we propose to use concept lattices as a tool for taxonomy building
(in the biclustering domain) and attribute exploration as means for
cross-domain taxonomy completion.
