Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications
Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications
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DOI：
10.1023/a:1009745219419
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发表时间：
1998-06-01
影响因子：
4.8
通讯作者：
Xu, XW
中科院分区：
文献类型：
--
作者：
Sander, J;Ester, M;Xu, XW
聚类算法DBSCAN依赖于基于密度的聚类概念，旨在发现任意形状的聚类以及区分噪声。在本文中，我们在两个重要的方向上推广了该算法。广义算法gdbscan可以根据点对象的空间属性和非空间属性对点对象和空间扩展对象进行聚类。此外，还介绍了使用2D点（天文学）、3D点（生物学）、5D点（地球科学）和2D多边形（地理）的四种应用，展示了GDBSCAN对现实世界问题的适用性。
The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we generalize this algorithm in two important directions. The generalized algorithm-called GDBSCAN-can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes. In addition, four applications using 2D points (astronomy), 3D points (biology), 5D points (earth science) and 2D polygons (geography) are presented, demonstrating the applicability of GDBSCAN to real-world problems.