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| Online Association Rules× | Apriori Algoritmen× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 1996 | 1994 |
| Ophavsperson≠ | Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. | Agrawal, R. & Srikant, R. |
| Type≠ | Incremental / streaming pattern mining | Frequent itemset and association rule mining algorithm |
| Oprindelig kilde≠ | 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 ↗ |
| Aliasser | Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM | Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining |
| Relaterede | 5 | 5 |
| Resumé≠ | 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. |
| ScholarGateDatasæt ↗ |
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