Porovnat metody

	Asociační dolování pravidel (Apriori)	K-Means shlukování
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	1994	1967
Tvůrce	Rakesh Agrawal & Ramakrishnan Srikant	MacQueen, J.
Typ	Unsupervised pattern discovery algorithm	Partitional clustering (centroid-based)
Původní zdroj	Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
Další názvy	Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Příbuzné	3	3
Shrnutí	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.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.