Salīdzināt metodes

	Secvenču modeļu ieguve	Procesu ieguve (Process Mining)
Nozare	Mašīnmācīšanās	Procesu ieguve
Saime	Machine learning	Process / pipeline
Izcelsmes gads	1995	2016
Autors	Rakesh Agrawal & Ramakrishnan Srikant	Wil van der Aalst
Tips	Unsupervised pattern discovery	Data-driven process analysis technique
Pirmavots	Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. IEEE International Conference on Data Engineering (ICDE), 3–14.	van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action (2nd ed.). Springer. ISBN: 978-3-662-49850-7
Citi nosaukumi	Sequence Pattern Mining, Sequential Data Mining, Temporal Pattern Mining, Ardışık Örüntü Madenciliği	Workflow Mining, Event Log Analysis, Process Discovery, Süreç Madenciliği
Saistītās	3	2
Kopsavilkums	Sequential Pattern Mining discovers ordered patterns that recur across multiple event sequences in a database. Introduced by Agrawal and Srikant in 1995, it extends association-rule mining to time-ordered transactions. A pattern is frequent when it appears as an ordered subsequence in at least a user-specified fraction of all sequences. The method is widely applied wherever the order of events carries meaning, such as customer purchase histories, clickstream logs, electronic health records, and DNA sequence analysis.	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.