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Is pushing constraints deeply into the mining algorithms really what we want?: an alternative approach for association rule mining, and . SIGKDD Explor. Newsl., 4 (1): 50--55 (June 2002)CPD: Crowd-based Pothole Detection., , , and . VEHITS, page 33-42. SciTePress, (2019)Outlier Detection by Rareness Assumption., and . GI Jahrestagung (1), volume P-50 of LNI, page 244-248. GI, (2004)Algorithms for association rule mining — a general survey and comparison, , and . SIGKDD Explor. Newsl., 2 (1): 58--64 (2000)When Distribution is Part of the Semantics: A New Problem Class for Distributed Knowledge Discovery, , and . 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, , and . SIGKDD Explorations, 2 (1): 58--64 (2000)Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today's Approaches, and . Proceedings of the 4th European Symposium on Principles of Data Mining and Knowledge Discovery, (2000)Deriving HD maps for highly automated driving from vehicular probe data., , , , , , , , and . ITSC, page 1745-1752. IEEE, (2016)The Atlas of Lane Changes: Investigating Location-Dependent Lane Change Behaviors Using Measurement Data from a Customer Fleet., , , and . ITSC, page 1225-1232. IEEE, (2021)Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior., , , and . ITSC, page 1-7. IEEE, (2020)