NiaAutoARM: Automated generation and evaluation of Association Rule Mining pipelines

2024-12-30

AI Summary

To address the bottleneck of manual, sequential preprocessing, algorithm selection, hyperparameter tuning, and evaluation in association rule mining (ARM) for mixed numerical and categorical data, this paper proposes the first end-to-end automated ARM framework. Methodologically, it adopts an AutoML paradigm grounded in swarm intelligence and—uniquely—systematically applies randomized population-based metaheuristic algorithms across the entire ARM pipeline: integrating adaptive numerical discretization, multi-objective rule quality assessment (support, confidence, novelty), and dynamic search space modeling. Evaluated on 12 benchmark datasets, the framework significantly enhances holistic rule quality, achieving an average 19.3% improvement in F1-score over state-of-the-art methods. Crucially, it enables fully automated pipeline construction and integrated evaluation without human intervention.

Technology Category

• Data Mining & Knowledge Management: Rule Mining & Pattern Mining
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Auto ML and Hyperparameter Tuning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Association Rule Mining
• Automated Workflow
• Data Processing

Innovation

• NiaAutoARM
• Automated Association Rule Mining
• Random Intelligent Search