NiaAutoARM: Automated generation and evaluation of Association Rule Mining pipelines

TL;DR

This paper introduces NiaAutoARM, an automated machine learning approach that constructs and evaluates association rule mining pipelines involving numerical and categorical data, streamlining the complex process.

Contribution

It presents a novel stochastic population-based meta-heuristic method for fully automating association rule mining pipeline construction.

Findings

Effective pipeline construction demonstrated through experiments

Automated approach reduces manual effort and expertise needed

Improves discovery of meaningful associations in mixed data types

Abstract

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.