Sammenlign metoder

	Semi-overvåget Apriori-algoritme	FP-Growth (Frequent Pattern Growth)
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1999–2005	2000
Ophavsperson	Extended from Agrawal & Srikant (1994); constrained variants developed by Liu, Hsu & Ma (1999) and others	Jiawei Han, Jian Pei & Yiwen Yin
Type	Constrained association rule mining algorithm	Frequent-itemset mining algorithm
Oprindelig kilde	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.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Aliasser	constrained Apriori, semi-supervised ARM, knowledge-guided Apriori, labeled-constraint Apriori	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Relaterede	4	4
Resumé	The Semi-supervised Apriori algorithm extends the classic Apriori frequent-itemset miner by injecting background knowledge or labeled constraints — such as must-link pairs, forbidden items, or user-specified minimum support thresholds per group — to bias discovery toward practically meaningful association rules and reduce the search space.	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.