Efficient Visualization of Association Rule Mining Using the Trie of Rules

Mikhail Kudriavtsev, Andrew McCarren, Hyowon Lee, Marija Bezbradica

2024

Abstract

Association Rule Mining (ARM) is a popular technique in data mining and machine learning for uncovering meaningful relationships within large datasets. However, the extensive number of generated rules presents significant challenges for interpretation and visualization. Effective visualization must not only be clear and informative but also efficient and easy to learn. Existing visualization methods often fall short in these areas. In response, we propose a novel visualization technique called the ”Trie of Rules.” This method adapts the Frequent Pattern Tree (FP-tree) structure to visualize association rules efficiently, capturing extensive information while maintaining clarity. Our approach reveals hidden insights such as clusters and substitute items, and introduces a unique feature for calculating confidence in rules with compound consequents directly from the graph structure. We conducted a comprehensive evaluation using a survey where we measured cognitive load to calculate the efficiency and learnability of our methodology. The results indicate that our method significantly enhances the interpretability and usability of ARM visualizations.

Paper Citation

in Harvard Style

Kudriavtsev M., McCarren A., Lee H. and Bezbradica M. (2024). Efficient Visualization of Association Rule Mining Using the Trie of Rules. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 72-80. DOI: 10.5220/0012995500003838

in Bibtex Style

@conference{kdir24,
author={Mikhail Kudriavtsev and Andrew McCarren and Hyowon Lee and Marija Bezbradica},
title={Efficient Visualization of Association Rule Mining Using the Trie of Rules},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={72-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012995500003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Efficient Visualization of Association Rule Mining Using the Trie of Rules
SN - 978-989-758-716-0
AU - Kudriavtsev M.
AU - McCarren A.
AU - Lee H.
AU - Bezbradica M.
PY - 2024
SP - 72
EP - 80
DO - 10.5220/0012995500003838
PB - SciTePress