Measuring clustering in 2dv space
Measuring clustering in 2dv space
复制标题
测量 2dv 空间中的聚类
DOI：
10.1111/j.1365-2966.2009.15540.x
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发表时间：
2009
影响因子：
4.8
通讯作者：
A. Cartwright
中科院分区：
文献类型：
--
作者：
A. Cartwright
统计描述符是一种强大且有用的工具，用于区分和量化对象（例如疏散星团）中的径向或多尺度聚类程度。计算公式为 m/s，其中 是最小生成树的平均边长度， 是簇成员之间的平均距离或相关长度。仅使用二维位置数据获得。在这里，我们研究了三维的性能，无论是当真正的三维数据可用时，还是当簇组件的径向速度用作位置的代理时：这称为 2dv 空间。真正的三维数据提高了分辨率，并且作为聚类的诊断指标，散点图与事实证明是解释信息的特别清晰的方法。当使用 2dv 信息时，结果并不令人满意，因为当使用 2dv 信息时，单独使用 2d 信息可以清楚地区分的簇类型的数据会变得重叠和混乱。因此，我们建议使用 2d 方法，除非集群成员的真实 3d 位置可用。特别建议使用对比图，因为与单独使用对比图相比，可以增加聚类类型之间的额外区分
The statistical descriptor is a robust and useful tool for distinguishing and quantifying the degree of radial or multiscale clustering in objects such as open clusters. is calculated as m/s, where is the mean edge length of the minimum spanning tree and is the mean distance between cluster members, or correlation length. is obtained using only two-dimensional position data. Here, we investigate the performance of in three dimensions, both when true three-dimensional data are available and when the radial velocity of cluster components is used as a proxy for position: this is known as 2dv space. True three-dimensional data offer an improvement in the resolution of and as diagnostic indicators of clustering, a scatter plot of versus proving to be a particularly clear method of interpreting the information. Results are not satisfactory when 2dv information is used, as the data from cluster types which are clearly distinguishable using 2d information alone become overlapping and confused when 2dv information is used. We therefore recommend that the 2d method is used, unless true 3d positions of cluster members are available. The use of the versus plot is particularly recommended, as adding extra discrimination between cluster types, compared with that achieved using alone