Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Apriori algoritms× | Tiešsaistes apguve× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1994 | 1958–2000s |
| Autors≠ | Agrawal, R. & Srikant, R. | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Tips≠ | Frequent itemset and association rule mining algorithm | Learning paradigm (sequential model update) |
| Pirmavots≠ | 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 ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Citi nosaukumi | Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining | incremental learning, sequential learning, streaming learning, online machine learning |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | 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. | Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight. |
| ScholarGateDatu kopa ↗ |
|
|