Sammenlign metoder

	Process Mining	Fællesskabsdetektion
Fagområde	Process mining	Netværksanalyse
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	2016	2002–2019 (algorithm family)
Ophavsperson	Wil van der Aalst	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
Type	Data-driven process analysis technique	Graph-partitioning / clustering algorithm family
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.
Aliasser	Workflow Mining, Event Log Analysis, Process Discovery, Süreç Madenciliği	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
Relaterede	2	5
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?