Soft Set ARM Yields Compact Interpretable Patterns Under Uncertainty

DOI

10.2991/978-2-38476-589-8_61How to use a DOI?

Keywords

Soft Sets; Association Rules; Maximal Rules

Abstract

General Background Association rule mining is widely used to identify co-occurrence structures in transactional and event data, but classical Boolean formulations are limited when data are uncertain, incomplete, multi-valued, hierarchical, or temporally varying. Specific Background Soft set theory offers a parameterized representation of uncertainty and has been integrated with association rule mining to address these limitations without relying on membership functions or equivalence relations. Knowledge Gap However, the literature remains fragmented across foundational models, algorithmic variants, hybrid formulations, and application domains, making the overall development of this area difficult to assess systematically. Aims This paper reviews and synthesizes 24 Scopus-indexed studies to clarify conceptual foundations, organize integration strategies, map application areas, and identify open research gaps. Results The synthesis shows that soft-set-based association rule mining can reproduce or improve classical and rough approaches, reduce runtime, and control rule proliferation through maximal rules and parameter reduction. Hybrid fuzzy-soft, temporal, and N-soft formulations also support real-valued, noisy, time-aware, and multi-group data across education, healthcare, logistics, bioinformatics, and web and text analytics. Novelty The study provides an integrated synthesis across foundational formulations, algorithmic enhancements, hybrid models, and domain-specific applications within a single review framework. Implications These findings position soft-set-based association rule mining as a mature and versatile framework for decision support while highlighting the need for scalable algorithms, automatic parameter learning, deeper hybridization, and rigorous benchmarking.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zahra Arwananing Tyas
AU  - Mustafa Mat Deris
AU  - Iwan Tri Riyadi Yanto
PY  - 2026
DA  - 2026/06/18
TI  - Soft Set ARM Yields Compact Interpretable Patterns Under Uncertainty
BT  - Proceedings of the 1st International Conference on Communication and Digital Multimedia 2025 (ICCDM 2025)
PB  - Atlantis Press
SP  - 753
EP  - 760
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-589-8_61
DO  - 10.2991/978-2-38476-589-8_61
ID  - Tyas2026
ER  -