Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Asociatīvo likumu ieguve (Apriori)× | Procesu ieguve (Process Mining)× | |
|---|---|---|
| Nozare≠ | Mašīnmācīšanās | Procesu ieguve |
| Saime≠ | Machine learning | Process / pipeline |
| Izcelsmes gads≠ | 1994 | 2016 |
| Autors≠ | Rakesh Agrawal & Ramakrishnan Srikant | Wil van der Aalst |
| Tips≠ | Unsupervised pattern discovery algorithm | Data-driven process analysis technique |
| Pirmavots≠ | Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216. DOI ↗ | van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action (2nd ed.). Springer. ISBN: 978-3-662-49850-7 |
| Citi nosaukumi | Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis | Workflow Mining, Event Log Analysis, Process Discovery, Süreç Madenciliği |
| Saistītās≠ | 3 | 2 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
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