Compară metode

	Reguli de asociere	Clustering K-means	Învățare online
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	1993	1967 (formalized 1982)	1958–2000s
Autorul original	Agrawal, R., Imielinski, T., & Swami, A.	MacQueen, J. B.; Lloyd, S. P.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Tip	Unsupervised pattern discovery	Partitional clustering	Learning paradigm (sequential model update)
Sursa seminală	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Denumiri alternative	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	incremental learning, sequential learning, streaming learning, online machine learning
Înrudite	4	4	6
Rezumat	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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.