  Biclustering techniques have been widely used to identify homogeneous
subgroups within large data matrices, such as subsets of genes similarly
expressed across subsets of patients. Mining a max-sum sub-matrix is a related
but distinct problem for which one looks for a (non-necessarily contiguous)
rectangular sub-matrix with a maximal sum of its entries. Le Van et al. (Ranked
Tiling, 2014) already illustrated its applicability to gene expression analysis
and addressed it with a constraint programming (CP) approach combined with
large neighborhood search (CP-LNS). In this work, we exhibit some key
properties of this NP-hard problem and define a bounding function such that
larger problems can be solved in reasonable time. Two different algorithms are
proposed in order to exploit the highlighted characteristics of the problem: a
CP approach with a global constraint (CPGC) and mixed integer linear
programming (MILP). Practical experiments conducted both on synthetic and real
gene expression data exhibit the characteristics of these approaches and their
relative benefits over the original CP-LNS method. Overall, the CPGC approach
tends to be the fastest to produce a good solution. Yet, the MILP formulation
is arguably the easiest to formulate and can also be competitive.
