Compară metode

	FP-Growth (Creștere Frecventă a Pattern-urilor)	Clustering K-Means
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2000	1967
Autorul original	Jiawei Han, Jian Pei & Yiwen Yin	MacQueen, J.
Tip	Frequent-itemset mining algorithm	Partitional clustering (centroid-based)
Sursa seminală	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
Denumiri alternative	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Înrudite	4	3
Rezumat	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 Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.