Porovnat metody

	Asociační pravidla	K-means Shlukování
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	1993	1967 (formalized 1982)
Tvůrce	Agrawal, R., Imielinski, T., & Swami, A.	MacQueen, J. B.; Lloyd, S. P.
Typ	Unsupervised pattern discovery	Partitional clustering
Původní zdroj	Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Další názvy	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Příbuzné	4	4
Shrnutí	Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.