Compară metode

	Reguli de asociere	Clustering K-means	Învățare semi-supervizată
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	1993	1967 (formalized 1982)	1970s–2006 (formalized)
Autorul original	Agrawal, R., Imielinski, T., & Swami, A.	MacQueen, J. B.; Lloyd, S. P.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tip	Unsupervised pattern discovery	Partitional clustering	Learning paradigm
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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
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	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Înrudite	4	4	5
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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.