ADCN: an anisotropic density-based clustering algorithm

Gengchen Mai, Krzysztof Janowicz, Yingjie Hu, Song Gao

Abstract: In this work we introduce an anisotropic density-based clustering algorithm. It outperforms DBSCAN and OPTICS for the detection of anisotropic spatial point patterns and performs equally well in cases that do not explicitly benefit from an anisotropic perspective. ADCN has the same time complexity as DBSCAN and OPTICS, namely O(n log n) when using a spatial index, O(n2) otherwise.