Сравнение на методи

	Откриване на асоциативни правила (Apriori)	Дърво на решенията
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	1994	1984
Създател	Rakesh Agrawal & Ramakrishnan Srikant	Breiman, Friedman, Olshen & Stone
Тип	Unsupervised pattern discovery algorithm	Recursive partitioning (if-then rules)
Основополагащ източник	Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Други названия	Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Свързани	3	5
Резюме	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.