The Numerical Association Rule Mining paradigm that includes concurrent dealing with numerical and categorical attributes is beneficial for discovering associations from datasets consisting of both features. The process is not considered as easy since it incorporates several processing steps running sequentially that form an entire pipeline, e.g., preprocessing, algorithm selection, hyper-parameter optimization, and the definition of metrics evaluating the quality of the association rule. In this paper, we proposed a novel Automated Machine Learning method, NiaAutoARM, for constructing the full association rule mining pipelines based on stochastic population-based meta-heuristics automatically. Along with the theoretical representation of the proposed method, we also present a comprehensive experimental evaluation of the proposed method.

翻译：包含数值与分类属性协同处理的数值关联规则挖掘范式，对于从兼具两类特征的数据集中发现关联规则具有显著优势。该过程因涉及多个顺序执行的处理步骤（如数据预处理、算法选择、超参数优化以及关联规则质量评估指标的定义）而构成完整流程，故不被视为简易任务。本文提出一种新颖的自动化机器学习方法NiaAutoARM，该方法基于随机群体智能元启发式算法，可自动构建完整的关联规则挖掘流程。除对所提方法进行理论阐述外，本文还提供了全面的实验评估。