Сравнение на методи

	Алгоритъм Apriori	Правила за асоциация	K-means клъстеризация
Област	Машинно обучение	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning	Machine learning
Година на възникване	1994	1993	1967 (formalized 1982)
Създател	Agrawal, R. & Srikant, R.	Agrawal, R., Imielinski, T., & Swami, A.	MacQueen, J. B.; Lloyd, S. P.
Тип	Frequent itemset and association rule mining algorithm	Unsupervised pattern discovery	Partitional clustering
Основополагащ източник	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.	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.
Други названия	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Свързани	5	4	4
Резюме	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.	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.