Сравнение методов

	Алгоритм Apriori	Кластеризация методом k-средних	Онлайн-обучение
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	1994	1967 (formalized 1982)	1958–2000s
Автор метода	Agrawal, R. & Srikant, R.	MacQueen, J. B.; Lloyd, S. P.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Тип	Frequent itemset and association rule mining algorithm	Partitional clustering	Learning paradigm (sequential model update)
Основополагающий источник	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Другие названия	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	incremental learning, sequential learning, streaming learning, online machine learning
Связанные	5	4	6
Сводка	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.	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.