Methoden vergleichen

	Online Association Rules	Assoziationsregeln
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1996	1993
Urheber	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Agrawal, R., Imielinski, T., & Swami, A.
Typ	Incremental / streaming pattern mining	Unsupervised pattern discovery
Wegweisende Quelle	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. (1996). Maintenance of discovered association rules in large databases: an incremental updating technique. In Proceedings of the 12th International Conference on Data Engineering (ICDE 1996), pp. 106–114. IEEE.	Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.
Aliasnamen	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	market basket analysis, association rule mining, frequent itemset mining, affinity analysis
Verwandt	5	4
Zusammenfassung	Online association rule mining discovers if-then patterns (e.g., buying bread implies buying butter) from transactional data that arrives incrementally or as a stream, updating existing rules and item counts without re-scanning the entire historical database each time new records arrive.	Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.