DiSARM：结合 LLM 与统计可靠性的关联规则挖掘框架
DiSARM: Diverse and Surprising Association Rule Mining with Large Language Models
数据挖掘圈的老方法关联规则挖掘遇上了 LLM：用模型的世界知识判断哪些规则真正"有趣"，又不放弃统计检验。做 EDA 或 BI 分析的同学可以看看这篇，论文给了在三个交易数据集上的实测结果，思路可以直接搬到自己的数据上。
论文提出混合框架 DiSARM，用于交易型数据的探索性分析（EDA）中的关联规则挖掘。核心思路是让 LLM 评估规则的"主观有趣性"，弥补传统客观度量难以发现意外模式的问题。
方法上，DiSARM 不只依赖 LLM，而是在传统 ARM 高效挖掘候选规则的基础上，用 LLM 的世界知识来筛选规则，同时保留统计可靠性，并推动所选规则之间的多样性。这样规避了纯 LLM 判定在可扩展性和统计可信度上的缺陷。
作者在三个交易数据集上做了实验，结果显示框架能兼顾统计可靠性、惊喜度与多样性。对做数据挖掘或 EDA 的团队来说，这提供了一条把 LLM 融入经典数据挖掘流程的可复用路径。
DiSARM: Diverse and Surprising Association Rule Mining with Large Language Models
Exploratory Data Analysis (EDA) aims to discover informative patterns in large datasets. For transactional data, Association Rule Mining (ARM) is an established approach that facilitates EDA by efficiently generating candidate patterns. Beyond efficient mining, identifying informative rules has also been extensively studied through both objective and subjective interestingness measures. Recent advances in Large Language Models (LLMs) offer new opportunities for assessing subjective interestingness using world knowledge. Nevertheless, relying solely on LLMs raises concerns about scalability and statistical reliability. Building on prior ARM research, we propose a hybrid framework, DiSARM, that leverages the world knowledge of LLMs to assess the subjective interestingness of rules, while maintaining statistical reliability and promoting diversity among the selected rules. Across three transactional datasets, experiments demonstrate that DiSARM effectively combines statistical reliability with surprise and diversity, offering a promising approach to ADE from transactional data.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：关联规则挖掘、LLM、探索性数据分析、DiSARM，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Lijia Liu, Hisashi Kashima），经 AITOP 多维评分管线结构化提炼。