Comparar métodos

	FP-Growth (Frequent Pattern Growth)	Agrupamento K-means
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2000	1967 (formalized 1982)
Autor original	Jiawei Han, Jian Pei & Yiwen Yin	MacQueen, J. B.; Lloyd, S. P.
Tipo	Frequent-itemset mining algorithm	Partitional clustering
Fonte seminal	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.
Outros nomes	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Relacionados	4	4
Resumo	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.