| Title: | niarules: advancing interpretable machine learning through numerical association rule mining and 3D coral plot visualization |
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| Authors: | ID Fister, Iztok (Author)
ID Emsenhuber, Gerlinde (Author)
ID Plümer, Jan Hendrik (Author)
ID Fister, Iztok (Author)
ID Holzinger, Andreas (Author) |
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| Files: |  1-s2.0-S2352711025004364-main.pdf (1,71 MB)
MD5: 6D070C62FBA0CDED220DEAA3974BD9D8
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.03 - Other scientific articles |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | Numerical association rule mining remains comparatively underexplored in interpretable machine learning, largely due to the challenges of handling continuous variables and the limited availability of effective visualization techniques. We introduce niarules, an open-source R package that provides a complete and extensible pipeline for numerical association rule mining, complemented by advanced post-processing and interactive 3D visualization. The package integrates bio-inspired optimization-based rule mining methods within a modular architecture that encompasses data preprocessing, rule mining, and visualization. A novel radial layout engine, implemented in C++, generates Coral Plots, which depict rules sharing a common consequent as radial trees. This design facilitates intuitive exploration of antecedent specificity, alongside key quality measures such as support, confidence, and lift. By combining methodological innovation with user-friendly visualization, niarules lowers the entry barrier to numerical association rule mining and supports the development of explainable AI systems for numerical datasets. |
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| Keywords: | association rule mining, interpretable machine learning, numerical association rule mining, coral plot visualization |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 01.02.2026 |
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| Publisher: | Elsevier B.V. |
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| Year of publishing: | 2026 |
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| Number of pages: | 7 str. |
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| Numbering: | Vol. 33, [article no.] 102470 |
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| PID: | 20.500.12556/DKUM-97012  |
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| UDC: | 004.8 |
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| ISSN on article: | 2352-7110 |
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| COBISS.SI-ID: | 267744259 |
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| DOI: | 10.1016/j.softx.2025.102470 |
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| Copyright: | © 2025 The Author(s). |
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| Publication date in DKUM: | 11.02.2026 |
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| Views: | 183 |
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| Downloads: | 7 |
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| Metadata: |    |
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| Categories: | Misc.
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