Porovnat metody

	Poloautomatický algoritmus Apriori	Semisupervisední učení
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	1999–2005	1970s–2006 (formalized)
Tvůrce	Extended from Agrawal & Srikant (1994); constrained variants developed by Liu, Hsu & Ma (1999) and others	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Constrained association rule mining algorithm	Learning paradigm
Původní zdroj	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Další názvy	constrained Apriori, semi-supervised ARM, knowledge-guided Apriori, labeled-constraint Apriori	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Příbuzné	4	5
Shrnutí	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.