Sammenlign metoder

	Emerging Pattern Mining	FP-Growth (Frequent Pattern Growth)
Fagfelt	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Opprinnelsesår	1999	2000
Opphavsperson	Guozhu Dong & Jinyan Li	Jiawei Han, Jian Pei & Yiwen Yin
Type	Supervised pattern discovery	Frequent-itemset mining algorithm
Opprinnelig kilde	Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. ACM SIGKDD, 43–52.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Alias	EP Mining, Contrast Pattern Mining, Differential Pattern Mining, Yükselen Örüntü Madenciliği	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Relaterte	3	4
Sammendrag	Emerging Pattern Mining (EPM) is a contrast-based data mining technique that identifies itemsets whose support increases significantly — or jumps from zero — when moving from one dataset (or class) to another. Introduced by Dong and Li in 1999, it is primarily used in classification, anomaly detection, and trend analysis tasks where discovering discriminative patterns between two populations or time periods is the central objective.	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.