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Is pushing constraints deeply into the mining algorithms really what we want?: an alternative approach for association rule mining, и . SIGKDD Explor. Newsl., 4 (1): 50--55 (июня 2002)CPD: Crowd-based Pothole Detection., , , и . VEHITS, стр. 33-42. SciTePress, (2019)Outlier Detection by Rareness Assumption., и . GI Jahrestagung (1), том P-50 из LNI, стр. 244-248. GI, (2004)When Distribution is Part of the Semantics: A New Problem Class for Distributed Knowledge Discovery, , и . Proceedings of the PKDD 2001 Workshop on Ubiquitous Data Mining for Mobile and Distributed Environments, (2001)They introduce a scenario where the semantics is part of the modelling process. A common pit fall is to view the distribution as a techical issue, but that does usually not suffice. Their concept is called Semantically Distributed Knowledge Discovery. Distribution is an integral part of the problem when at least some of the features depends on the context..Algorithms for association rule mining — a general survey and comparison, , и . SIGKDD Explor. Newsl., 2 (1): 58--64 (2000)Mining Association Rules: Deriving a Superior Algorithm by Analysing Today's Approaches, , и . Principles of Data Mining and Knowledge Discovery, 4th European Conference, PKDD 2000, 1910, стр. 159--168. Springer, (2000)Identifying Scenarios in Field Data to Enable Validation of Highly Automated Driving Systems., , , , , , и . VEHITS, стр. 134-142. SCITEPRESS, (2022)A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions., , , , и . SSCI, стр. 2739-2745. IEEE, (2020)Statistical Modelling of Driving Scenarios in Road Traffic using Fleet Data of Production Vehicles., , и . CoRR, (2024)Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today's Approaches, и . Proceedings of the 4th European Symposium on Principles of Data Mining and Knowledge Discovery, (2000)