方法对比
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| 可解释关联规则× | Apriori算法× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1993 (rules); 2010s (XAI framing) | 1994 |
| 提出者≠ | Agrawal, R., Imielinski, T., & Swami, A. (foundational); XAI framing: broader community (2010s–present) | Agrawal, R. & Srikant, R. |
| 类型≠ | Interpretable pattern mining / XAI technique | Frequent itemset and association rule mining algorithm |
| 开创性文献≠ | Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216. DOI ↗ | Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499. link ↗ |
| 别名 | XAI association rules, interpretable association rules, rule-based explanation mining, transparent association rule learning | Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining |
| 相关≠ | 6 | 5 |
| 摘要≠ | Explainable Association Rules leverages the inherently symbolic, if-then structure of association rule mining to provide human-readable explanations of data patterns or black-box model decisions. Because each rule explicitly states its antecedent and consequent together with support, confidence, and lift, the outputs are natively interpretable without requiring a secondary post-hoc surrogate. | The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those patterns. |
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