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	Semi-supervised Apriori-algoritm	Association Rule Mining (Apriori)
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	1999–2005	1994
Upphovsperson	Extended from Agrawal & Srikant (1994); constrained variants developed by Liu, Hsu & Ma (1999) and others	Rakesh Agrawal & Ramakrishnan Srikant
Typ	Constrained association rule mining algorithm	Unsupervised pattern discovery algorithm
Ursprungskälla	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.	Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216.
Alias	constrained Apriori, semi-supervised ARM, knowledge-guided Apriori, labeled-constraint Apriori	Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis
Närliggande	4	3
Sammanfattning	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.	Association Rule Mining is an unsupervised data-mining technique that discovers co-occurrence patterns among items in transactional datasets. Formally introduced by Agrawal, Imieliński, and Swami in 1993, and refined with the landmark Apriori algorithm by Agrawal and Srikant in 1994, it identifies rules of the form X ⇒ Y — meaning that transactions containing itemset X tend to also contain itemset Y — quantified by support, confidence, and lift.