Sammenlign metoder

	Process Mining	Fællesskabsdetektion	Sekventiel mønsterudvinding
Fagområde	Process mining	Netværksanalyse	Maskinlæring
Familie	Process / pipeline	Process / pipeline	Machine learning
Oprindelsesår	2016	2002–2019 (algorithm family)	1995
Ophavsperson	Wil van der Aalst	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	Rakesh Agrawal & Ramakrishnan Srikant
Type	Data-driven process analysis technique	Graph-partitioning / clustering algorithm family	Unsupervised pattern discovery
Oprindelig kilde	van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action (2nd ed.). Springer. ISBN: 978-3-662-49850-7	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.	Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. IEEE International Conference on Data Engineering (ICDE), 3–14.
Aliasser	Workflow Mining, Event Log Analysis, Process Discovery, Süreç Madenciliği	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	Sequence Pattern Mining, Sequential Data Mining, Temporal Pattern Mining, Ardışık Örüntü Madenciliği
Relaterede	2	5	3
Resumé	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.	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?	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.