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| 설명 가능한 FP-Growth× | Apriori 알고리즘× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000 (FP-Growth); XAI augmentation emerged ~2018–present | 1994 |
| 창시자≠ | Han, J., Pei, J., & Yin, Y. (FP-Growth); XAI augmentation from the interpretable ML community | Agrawal, R. & Srikant, R. |
| 유형≠ | Explainable frequent pattern mining | Frequent itemset and association rule mining algorithm |
| 원전≠ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. 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 ↗ |
| 별칭 | XAI-FP-Growth, interpretable frequent pattern mining, explainable frequent itemset mining, transparent FP-Growth | Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining |
| 관련 | 5 | 5 |
| 요약≠ | Explainable FP-Growth augments the classic FP-Growth frequent-pattern mining algorithm with post-hoc interpretability tools — such as rule importance scores, visual pattern trees, and counterfactual explanations — so analysts can not only discover frequent itemsets and association rules but also understand why specific patterns matter, which items drive rule confidence, and how to communicate findings transparently to stakeholders. | 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. |
| ScholarGate데이터셋 ↗ |
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