A Unified Approach to Biclustering Based on Formal Concept Analysis and Interval Pattern Structure.

In a matrix representing a numerical dataset, a bicluster is a submatrix whose cells exhibit similar behavior. Biclustering is naturally related to Formal Concept Analysis (FCA) where concepts correspond to maximal and closed biclusters in a binary dataset. In this paper, a unified characterization of biclustering algorithms is proposed using FCA and pattern structures, an extension of FCA for dealing with numbers and other complex data. Several types of biclusters - constant-column, constant-row, additive, and multiplicative - and their relation to interval pattern structures is presented.