So sánh phương pháp

	Khai phá mẫu tuần tự	Khai phá luật kết hợp (Apriori)	Process Mining
Lĩnh vực	Học máy	Học máy	Khai phá quy trình
Họ	Machine learning	Machine learning	Process / pipeline
Năm ra đời	1995	1994	2016
Người khởi xướng	Rakesh Agrawal & Ramakrishnan Srikant	Rakesh Agrawal & Ramakrishnan Srikant	Wil van der Aalst
Loại	Unsupervised pattern discovery	Unsupervised pattern discovery algorithm	Data-driven process analysis technique
Công trình gốc	Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. IEEE International Conference on Data Engineering (ICDE), 3–14.	Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216.	van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action (2nd ed.). Springer. ISBN: 978-3-662-49850-7
Tên gọi khác	Sequence Pattern Mining, Sequential Data Mining, Temporal Pattern Mining, Ardışık Örüntü Madenciliği	Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis	Workflow Mining, Event Log Analysis, Process Discovery, Süreç Madenciliği
Liên quan	3	3	2
Tóm tắt	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.	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.