A brief overview of swarm intelligence-based algorithms for numerical association rule mining

TL;DR

This paper reviews swarm intelligence algorithms for numerical association rule mining, highlighting their efficiency, historical development, and key features, while proposing a taxonomy and discussing future challenges.

Contribution

It provides a comprehensive historical overview and taxonomy of swarm intelligence-based algorithms specifically for numerical association rule mining.

Findings

Swarm intelligence algorithms are effective for numerical association rule mining.

A taxonomy categorizes these algorithms based on their features.

Future challenges in the field are identified and discussed.

Abstract

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.