  Motivation: A considerable number of data mining approaches for biomedical
data analysis, including state-of-the-art associative models, require a form of
data discretization. Although diverse discretization approaches have been
proposed, they generally work under a strict set of statistical assumptions
which are arguably insufficient to handle the diversity and heterogeneity of
clinical and molecular variables within a given dataset. In addition, although
an increasing number of symbolic approaches in bioinformatics are able to
assign multiple items to values occurring near discretization boundaries for
superior robustness, there are no reference principles on how to perform
multi-item discretizations.
  Results: In this study, an unsupervised discretization method, DI2, for
variables with arbitrarily skewed distributions is proposed. DI2 provides
robust guarantees of generalization by placing data corrections using the
Kolmogorov-Smirnov test before statistically fitting distribution candidates.
DI2 further supports multi-item assignments. Results gathered from biomedical
data show its relevance to improve classic discretization choices.
  Software: available at https://github.com/JupitersMight/DI2
