Methoden vergleichen

	Aktive Lernassoziationsregeln	FP-Growth (Frequent Pattern Growth)
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2010s	2000
Urheber	Dzyuba, V. & van Leeuwen, M.; Boley, M. et al.	Jiawei Han, Jian Pei & Yiwen Yin
Typ	Interactive pattern mining	Frequent-itemset mining algorithm
Wegweisende Quelle	Dzyuba, V., & van Leeuwen, M. (2017). Interactive Discovery of Interesting Association Rules by Subjective Interestingness. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Springer.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Aliasnamen	interactive association rule mining, active rule mining, query-driven association rule discovery, user-guided association rules	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Verwandt	5	4
Zusammenfassung	Active learning association rules combines the iterative query-and-label loop of active learning with association rule mining, allowing a human expert to guide the discovery process interactively. Instead of exhaustively enumerating all rules above a fixed support-confidence threshold, the system selects the most informative rule candidates and asks the user to judge their interestingness, focusing the search on subjectively useful 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.