Example of MIDClass flowchart on Breast Cancer 2 Dataset (data are partially shown).
Let 
denote the expression value of sample 
on the 
-th gene (an example of entry in M is shown as a black box). Samples are divided into classes corresponding to phenotypes disease. After discretization process, MIDClass constructs a matrix 
from 
by replacing each 
with the unique interval containing it. 
denotes an entry in 
(an example of entry in 
is shown as a black box). Then, MIDClass computes per class the possible sets of 
that are frequent and they have maximal size. MIDClass filters out gene expression intervals which size are below a given threshold. Since, association rules express interesting relationships between gene expressions and class labels, MIDClass uses them for classification. Therefore, MIDClass extracts a set of rules per class. Each rule has quantitative attributes on the antecedence part (i.e. discretized values) and one categorical attribute on the consequence side (i.e. the class 
). Finally, it returns only rules that have a maximal score. The score takes into account the number of items in each sample are contained in the rule together with the cardinality of the rule (the computation of the score is described in detailed in the Methods section).
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