Salīdzināt metodes

	Tiešsaistes asociāciju likumu atrašana	Asociācijas likumi	FP-Growth (biežo kopu augšana)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning	Machine learning
Izcelsmes gads	1996	1993	2000
Autors	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Agrawal, R., Imielinski, T., & Swami, A.	Jiawei Han, Jian Pei & Yiwen Yin
Tips	Incremental / streaming pattern mining	Unsupervised pattern discovery	Frequent-itemset mining algorithm
Pirmavots	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.	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.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Citi nosaukumi	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Saistītās	5	4	4
Kopsavilkums	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.	Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.	FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.