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| FP-Growth (Frequent Pattern Growth)× | Prozess-Mining× | |
|---|---|---|
| Fachgebiet≠ | Maschinelles Lernen | Process Mining |
| Familie≠ | Machine learning | Process / pipeline |
| Entstehungsjahr≠ | 2000 | 2016 |
| Urheber≠ | Jiawei Han, Jian Pei & Yiwen Yin | Wil van der Aalst |
| Typ≠ | Frequent-itemset mining algorithm | Data-driven process analysis technique |
| Wegweisende Quelle≠ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ | van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action (2nd ed.). Springer. ISBN: 978-3-662-49850-7 |
| Aliasnamen | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme | Workflow Mining, Event Log Analysis, Process Discovery, Süreç Madenciliği |
| Verwandt≠ | 4 | 2 |
| Zusammenfassung≠ | 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. |
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