Compară metode

	Reguli de asociere	Algoritmul Apriori	Clustering K-means
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	1993	1994	1967 (formalized 1982)
Autorul original	Agrawal, R., Imielinski, T., & Swami, A.	Agrawal, R. & Srikant, R.	MacQueen, J. B.; Lloyd, S. P.
Tip	Unsupervised pattern discovery	Frequent itemset and association rule mining algorithm	Partitional clustering
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.	Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Denumiri alternative	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Înrudite	4	5	4
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.	The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those patterns.	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.