Rough set theory was proposed by Pawlak [1]. Rough
set is new mathematical tool for dealing with uncertainty
and it is widely applied to pattern recognition, image
processing, feature selection, rule extraction, decision
supporting, granular computing, data mining and
knowledge discovery from large data sets.
In modern fuzzy logic theory (see Refs. [2~12]), t-norm
based logic usually refers to residuated systems of fuzzy
logic with t-norm based semantics, i.e. where the
conjunction connective is interpreted by a t-norm and the
implication operator by its residuum. Hajek [2] introduces
a logic system, named BL, which is the logic of all
continuous t-norms and of their residua. Esteva et al. in
Ref. [3] propose monoidal t-norm based logic (MTL), and
conjecture that MTL is the logic of left-continuous t-norms
and of their residuals. This conjecture was shown to be
true in Ref. [4]. The fuzzy logic system the logic of
nilpotent minimum (NM) is an important schematic
extension of MTL, and further studies in many literatures
(see Refs. [5–6]).