방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 온라인 연관 규칙× | Apriori 알고리즘× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1996 | 1994 |
| 창시자≠ | Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. | Agrawal, R. & Srikant, R. |
| 유형≠ | Incremental / streaming pattern mining | Frequent itemset and association rule mining algorithm |
| 원전≠ | Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. (1996). Maintenance of discovered association rules in large databases: an incremental updating technique. In Proceedings of the 12th International Conference on Data Engineering (ICDE 1996), pp. 106–114. IEEE. link ↗ | 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 | Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM | Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining |
| 관련 | 5 | 5 |
| 요약≠ | Online association rule mining discovers if-then patterns (e.g., buying bread implies buying butter) from transactional data that arrives incrementally or as a stream, updating existing rules and item counts without re-scanning the entire historical database each time new records arrive. | 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 ↗ |
|
|