Сравнение методов

	FP-Рост (Рост часто встречаемых паттернов)	Кластеризация методом k-средних	Онлайн-обучение
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	2000	1967 (formalized 1982)	1958–2000s
Автор метода	Jiawei Han, Jian Pei & Yiwen Yin	MacQueen, J. B.; Lloyd, S. P.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Тип	Frequent-itemset mining algorithm	Partitional clustering	Learning paradigm (sequential model update)
Основополагающий источник	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Другие названия	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	incremental learning, sequential learning, streaming learning, online machine learning
Связанные	4	4	6
Сводка	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.	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.