  RIn-Close_CVC is an efficient (take polynomial time per bicluster), complete
(find all maximal biclusters), correct (all biclusters attend the user-defined
level of consistency) and non-redundant (all the obtained biclusters are
maximal and the same bicluster is not enumerated more than once) enumerative
algorithm for mining maximal biclusters with constant values on columns in
numerical datasets. Despite RIn-Close_CVC has all these outstanding properties,
it has a high computational cost in terms of memory usage because it must keep
a symbol table in memory to prevent a maximal bicluster to be found more than
once. In this paper, we propose a new version of RIn-Close_CVC, named
RIn-Close_CVC2, that does not use a symbol table to prevent redundant
biclusters, and keeps all these four properties. We also prove that these
algorithms actually possess these properties. Experiments are carried out with
synthetic and real-world datasets to compare RIn-Close_CVC and RIn-Close_CVC2
in terms of memory usage and runtime. The experimental results show that
RIn-Close_CVC2 brings a large reduction in memory usage and, in average,
significant runtime gain when compared to its predecessor.
