An Iterative Detection and Removal Method for Detecting Spatial Clusters of Different Densities
An Iterative Detection and Removal Method for Detecting Spatial Clusters of Different Densities
复制标题
一种检测不同密度空间簇的迭代检测和去除方法
DOI：
10.1111/tgis.12083
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发表时间：
2015-02-01
影响因子：
2.4
通讯作者：
Shi, Yan
中科院分区：
文献类型：
--
作者：
Liu, Qiliang;Tang, Jianbo;Shi, Yan
探索性空间数据分析的一个基本要素是发现空间点数据集中的聚类。当存在局域密度明显不同的团簇时，合适的密度水平的确定仍然是一个未解决的问题。基于此，本研究提出一种迭代侦测与移除的方法。在新方法的每个步骤中，有两个阶段。在检测阶段，密度水平被统计建模为由数据集中的点的数量和支持域控制的显著性水平，然后使用假设检验来检测高密度点。在去除阶段，利用Delaunay三角网对识别出的高密度点进行聚类和支持域构造，然后将高密度点及其支持域从数据集中去除。检测和移除操作被迭代地实现，直到没有高密度点可以被检测到。实验和比较表明，该方法，一方面，优于四个国家的最先进的方法检测复杂形状和不同密度的集群，另一方面，不需要用户指定的参数。此外，聚类的支持域对于空间分析也非常有用。
A fundamental element of exploratory spatial data analysis is the discovery of clusters in a spatial point dataset. When clusters with distinctly different local densities exist, the determination of suitable density level is still an unsolved problem. On that account, an iterative detection and removal method is proposed in this study. In each step of the novel method, there are two stages. In the detection stage, density level is statistically modeled as a significance level controlled by the number and support domain of the points in the dataset, and then a hypothesis test is used to detect the high-density points. In the removal stage, the Delaunay triangulation network is used to construct clusters and support domains for the identified high-density points, and then the high-density points and their support domains are removed from the dataset. The detection and removal operation are iteratively implemented until no high-density points can be detected. Experiments and comparisons show that the proposed method, on the one hand, outperforms four state-of-the-art methods for detecting clusters of complex shapes and diverse densities, and on the other hand, no user-specified parameters are required. In addition, the support domains of clusters are very useful for spatial analysis.