Usporedite metode

	Pravila udruživanja	Algoritam Apriori	Polunadzorirano učenje
Područje	Strojno učenje	Strojno učenje	Strojno učenje
Obitelj	Machine learning	Machine learning	Machine learning
Godina nastanka	1993	1994	1970s–2006 (formalized)
Tvorac	Agrawal, R., Imielinski, T., & Swami, A.	Agrawal, R. & Srikant, R.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Vrsta	Unsupervised pattern discovery	Frequent itemset and association rule mining algorithm	Learning paradigm
Temeljni izvor	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.	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
Drugi nazivi	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Srodne	4	5	5
Sažetak	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.	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.	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.