방법 비교

	Rule Induction	연관 규칙 마이닝(Apriori)	결정 트리
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1995	1994	1984
창시자	William W. Cohen	Rakesh Agrawal & Ramakrishnan Srikant	Breiman, Friedman, Olshen & Stone
유형	Supervised rule learning algorithm	Unsupervised pattern discovery algorithm	Recursive partitioning (if-then rules)
원전	Cohen, W. W. (1995). Fast effective rule induction. Proceedings of the 12th International Conference on Machine Learning, 115–123.	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.
별칭	RIPPER, Propositional Rule Learning, Kural Tümevarımı, Inductive Rule Learning	Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
관련	2	3	5
요약	Rule Induction, and specifically the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) algorithm, is a supervised machine learning method that learns a compact set of IF-THEN classification rules from labeled training data. Introduced by William W. Cohen in 1995, RIPPER applies a separate-and-conquer strategy combined with minimum description length (MDL) pruning to generate rules that are both accurate and interpretable, making it a landmark algorithm in the field of inductive rule learning.	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.