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Authors: Luiz Cintra 1 ; Rodigo Dias 2 and Rogerio Salvini 1

Affiliations: 1 Instituto de Informática, Universidade Federal de Goiás, Goiânia-GO, Brazil ; 2 Department of Psychiatry, University of Sao Paulo Medical School, São Paulo-SP, Brazil

Keyword(s): Association Rule Mining, Post-Processing, Knowledge Discovery.

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 interpretatio n 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. (More)

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Paper citation in several formats:
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 - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 119-130. DOI: 10.5220/0012465800003654

@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 - ICPRAM},
year={2024},
pages={119-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012465800003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - A Post-Processing Strategy for Association Rules in Knowledge Discovery
SN - 978-989-758-684-2
IS - 2184-4313
AU - Cintra, L.
AU - Dias, R.
AU - Salvini, R.
PY - 2024
SP - 119
EP - 130
DO - 10.5220/0012465800003654
PB - SciTePress

- Science and Technology Publications, Lda.
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