论文标题
简要概述了基于智能的数值关联规则挖掘算法
A brief overview of swarm intelligence-based algorithms for numerical association rule mining
论文作者
论文摘要
数值关联规则挖掘是关联规则挖掘的流行变体，其中处理数值属性而无需离散化。这意味着解决此问题的算法不仅可以直接使用，而且还可以使用数值属性。直到最近，这些算法中的很大一部分基于基于随机性的基于种群的范式。结果，进化和基于智能的算法在处理该问题方面显示出很高的效率。与此相符，本章的主要任务是对基于数值关联规则挖掘的基于群体智能的算法进行历史概述，并介绍这些算法的主要特征，以解决观察到的问题。根据本概述中发现的应用特征，提出了算法的分类学。挑战，将来等待，完成本文。
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.
