Módszerek összehasonlítása

	Félfelügyelt Asszociációs Szabályok	FP-Growth (Frequent Pattern Growth)
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2003–2010s	2000
Megalkotó	Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)	Jiawei Han, Jian Pei & Yiwen Yin
Típus	Pattern mining with partial supervision	Frequent-itemset mining algorithm
Alapmű	Liu, B., Hsu, W., & Ma, Y. (2003). Integrating Classification and Association Rule Mining. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pp. 339–346.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Alternatív nevek	semi-supervised ARM, label-guided association rule mining, constrained association rule mining, semi-supervised pattern discovery	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Kapcsolódó	4	4
Összefoglaló	Semi-supervised association rule mining extends classical association rule learning by incorporating a small amount of labeled data alongside a larger unlabeled dataset. It uses known class information or user-provided constraints to guide the discovery of rules that are both statistically frequent and semantically meaningful, bridging unsupervised pattern mining with light supervision.	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.