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<p>Based on a granularity tree, this paper investigates the changes in association rules before and after attribute granularity transformation in a formal context. It introduces zoom algorithms to update association rules. The zoom-in algorithm is applied to refine the attribute granularity from coarse to fine, while the zoom-out algorithm achieves the attribute granularity from fine to coarse. These zoom algorithms enable the direct manipulation of association rules in the original formal context, using concepts as a bridge to generate association rules in the new context. This approach eliminates the need for reconstructing the concept lattice when attribute granularity changes. Experimental results demonstrate that the algorithm proposed in this paper significantly reduces computational workload and shortens running time compared to the classical algorithm flow.</p>

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Zoom method for association rules in multi-granularity formal context

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • Lihui Niu,
  • Jusheng Mi,
  • Yuzhang Bai,
  • Zhongling Li,
  • Meizheng Li

摘要

Based on a granularity tree, this paper investigates the changes in association rules before and after attribute granularity transformation in a formal context. It introduces zoom algorithms to update association rules. The zoom-in algorithm is applied to refine the attribute granularity from coarse to fine, while the zoom-out algorithm achieves the attribute granularity from fine to coarse. These zoom algorithms enable the direct manipulation of association rules in the original formal context, using concepts as a bridge to generate association rules in the new context. This approach eliminates the need for reconstructing the concept lattice when attribute granularity changes. Experimental results demonstrate that the algorithm proposed in this paper significantly reduces computational workload and shortens running time compared to the classical algorithm flow.