  Numerical Association Rule Mining is a popular variant of Association Rule
Mining, where numerical attributes are handled without discretization. This
means that the algorithms for dealing with this problem can operate directly,
not only with categorical, but also with numerical attributes. Until recently,
a big portion of these algorithms were based on a stochastic nature-inspired
population-based paradigm. As a result, evolutionary and swarm
intelligence-based algorithms showed big efficiency for dealing with the
problem. In line with this, the main mission of this chapter is to make a
historical overview of swarm intelligence-based algorithms for Numerical
Association Rule Mining, as well as to present the main features of these
algorithms for the observed problem. A taxonomy of the algorithms was proposed
on the basis of the applied features found in this overview. Challenges,
waiting in the future, finish this paper.
