A Post-Processing Strategy for Association Rules in Knowledge Discovery

Luiz Cintra, Rodigo Dias, Rogerio Salvini

2024

Abstract

Association Rule Mining (ARM) is a traditional data mining method that describes associations among elements in transactional databases. A well-known problem of ARM is the large number of rules generated, requiring approaches to post-process these rules so that a human expert can analyze the associations found. In certain scenarios, experts focus on exploring a specific element within the data, and a search based on this item can help reduce the problem. Few methods concentrate on post-processing generated rules targeting a specific item of interest. This study aims to highlight relevant associations of a particular element in order to gain knowledge about its role through its interactions and relationships with other factors. The paper introduces a post-processing strategy for association rules, selecting and grouping rules pertinent to a specific item of interest as provided by a domain expert. Additionally, a graphical representation facilitates the visualization and interpretation of associations between rules and their groupings. A case study demonstrates the applicability of the proposed method, effectively reducing the number of relevant rules to a manageable level for expert analysis.

Paper Citation

in Harvard Style

Cintra L., Dias R. and Salvini R. (2024). A Post-Processing Strategy for Association Rules in Knowledge Discovery. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 119-130. DOI: 10.5220/0012465800003654

in Bibtex Style

@conference{icpram24,
author={Luiz Cintra and Rodigo Dias and Rogerio Salvini},
title={A Post-Processing Strategy for Association Rules in Knowledge Discovery},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={119-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012465800003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - A Post-Processing Strategy for Association Rules in Knowledge Discovery
SN - 978-989-758-684-2
AU - Cintra L.
AU - Dias R.
AU - Salvini R.
PY - 2024
SP - 119
EP - 130
DO - 10.5220/0012465800003654
PB - SciTePress