Сравнение методов

	Алгоритм Apriori	FP-Рост (Рост часто встречаемых паттернов)	Кластеризация методом k-средних
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	1994	2000	1967 (formalized 1982)
Автор метода	Agrawal, R. & Srikant, R.	Jiawei Han, Jian Pei & Yiwen Yin	MacQueen, J. B.; Lloyd, S. P.
Тип	Frequent itemset and association rule mining algorithm	Frequent-itemset mining algorithm	Partitional clustering
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.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Другие названия	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Связанные	5	4	4
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.	FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.	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.