  Data matrix having different sets of entities in its rows and columns are
known as two mode data or affiliation data. Many practical problems require to
find relationships between the two modes by simultaneously clustering the rows
and columns, a problem commonly known as biclustering. We propose a novel
biclustering algorithm by using matrix reordering approach introduced by
Cuthill-McKee's bandwidth minimization algorithm, and adapting it to operate on
non-square and non-binary matrices, without the need to know apriori the number
of naturally occurring biclusters. This transforms a two-mode matrix into
almost block diagonals, where the blocks indicate the clusters between the two
modes of the matrix. To optimize the bandwidth minimization problem, we adapted
the Biogeography-based Optimization algorithm using logistic equation to model
its migration rates. Preliminary studies indicate that this technique can
reveal the underlying biclusters in the data and has potential of further
research for two-mode data analysis.
