ADCN:基于各向异性密度的聚类算法

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI:10.1145/2996913.2996940

Gengchen Mai, K. Janowicz, Yingjie Hu, Song Gao

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引用次数: 5

摘要

本文介绍了一种基于各向异性密度的聚类算法。它在检测各向异性空间点模式方面优于DBSCAN和OPTICS，并且在不明显受益于各向异性视角的情况下表现同样良好。ADCN具有与DBSCAN和OPTICS相同的时间复杂度，使用空间索引时为O(n log n)，不使用空间索引时为O(n2)。

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ADCN: an anisotropic density-based clustering algorithm

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.

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Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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