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DBSCAN* [6] [7] is a variation that treats border points as noise, and this way achieves a fully deterministic result as well as a more consistent statistical interpretation of density-connected components. The quality of DBSCAN depends on the distance measure used in the function regionQuery(P,ε).
The R package "dbscan" includes a C++ implementation of OPTICS (with both traditional dbscan-like and ξ cluster extraction) using a k-d tree for index acceleration for Euclidean distance only. Python implementations of OPTICS are available in the PyClustering library and in scikit-learn .
Images manually labeled to show paths of individuals through crowds. ~ 150 Images with paths People tracking, aerial tracking 2012 [151] [152] M. Butenuth et al. Wilt Dataset Remote sensing data of diseased trees and other land cover. Various features extracted. 4899 Images Classification, aerial object detection 2014 [153] [154] B. Johnson
Deutsch: DBSCAN-Clusteranalyse auf einem Datensatz mit dichte-basierten Clustern. Hier passen Algorithmus und Datensatz perfekt zueinander (was aber in der Realität natürlich nicht immer so sein wird). Visualisiert mit ELKI.
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English: Cluster analysis with DBSCAN on a gaussian-distributed-based data set. Even with carefully chosen parameters minPts and ε {\displaystyle \varepsilon } , DBSCAN is unable to capture all clusters correctly at the same time, since the difference in density is too high and the separation too low.
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