  This paper further extends RIn-Close_CVC, a biclustering algorithm capable of
performing an efficient, complete, correct and non-redundant enumeration of
maximal biclusters with constant values on columns in numerical datasets. By
avoiding a priori partitioning and itemization of the dataset, RIn-Close_CVC
implements an online partitioning, which is demonstrated here to guide to more
informative biclustering results. The improved algorithm is called
RIn-Close_CVC3, keeps those attractive properties of RIn-Close_CVC, as formally
proved here, and is characterized by: a drastic reduction in memory usage; a
consistent gain in runtime; additional ability to handle datasets with missing
values; and additional ability to operate with attributes characterized by
distinct distributions or even mixed data types. The experimental results
include synthetic and real-world datasets used to perform scalability and
sensitivity analyses. As a practical case study, a parsimonious set of relevant
and interpretable mixed-attribute-type rules is obtained in the context of
supervised descriptive pattern mining.
