Salīdzināt metodes

	Asociatīvo likumu ieguve (Apriori)	Granulārā skaitļošana (informācijas granulēšana)
Nozare	Mašīnmācīšanās	Mīkstā skaitļošana
Saime	Machine learning	Machine learning
Izcelsmes gads	1994	1997
Autors	Rakesh Agrawal & Ramakrishnan Srikant	Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao
Tips	Unsupervised pattern discovery algorithm	Framework for multi-granularity information processing
Pirmavots	Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216.	Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127.
Citi nosaukumi	Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis	information granulation, computing with granules, three-way granular computing, tanecikli hesaplama
Saistītās	3	3
Kopsavilkums	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.	Granular computing is a problem-solving paradigm that processes information in 'granules' — clumps of objects drawn together by indistinguishability, similarity, or functionality — rather than at the level of individual data points. Articulated by Lotfi Zadeh in 1997 as fuzzy information granulation and developed into a broad framework, it provides a unifying umbrella over fuzzy sets, rough sets, and interval methods, letting analysis move to whichever level of detail a problem actually requires.