Compara mètodes

	Minera d'Associacions (Apriori)	FP-Growth (Frequent Pattern Growth)	Mineria de processos
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Mineria de processos
Família	Machine learning	Machine learning	Process / pipeline
Any d'origen	1994	2000	2016
Autor original	Rakesh Agrawal & Ramakrishnan Srikant	Jiawei Han, Jian Pei & Yiwen Yin	Wil van der Aalst
Tipus	Unsupervised pattern discovery algorithm	Frequent-itemset mining algorithm	Data-driven process analysis technique
Font seminal	Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action (2nd ed.). Springer. ISBN: 978-3-662-49850-7
Àlies	Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	Workflow Mining, Event Log Analysis, Process Discovery, Süreç Madenciliği
Relacionats	3	4	2
Resum	Association Rule Mining is an unsupervised data-mining technique that discovers co-occurrence patterns among items in transactional datasets. Formally introduced by Agrawal, Imieliński, and Swami in 1993, and refined with the landmark Apriori algorithm by Agrawal and Srikant in 1994, it identifies rules of the form X ⇒ Y — meaning that transactions containing itemset X tend to also contain itemset Y — quantified by support, confidence, and lift.	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.	Process Mining is a data-driven discipline that extracts knowledge about real-world processes from event logs recorded by information systems. Introduced systematically by Wil van der Aalst, with foundational workflow mining formalized in 2004 and consolidated in the 2016 textbook, the technique bridges data science and process management. It enables organizations to discover how processes actually execute, check whether execution conforms to prescribed models, and diagnose performance bottlenecks — all directly from digital traces.