RDCI：一种新的聚类分析方法及其在样品分子模拟中的应用
RDCI: A novel method of cluster analysis and applications thereof in sample molecular simulations
摘要：
本文提出了一种新的用于识别和定量描述三维空间中由N个目标组成的聚类的方法--约简聚类识别法（RDCI）。该方法包括找到一条尽可能短的路径，连接对象，然后跟踪子群i-n，i-n+1，.，i+n，2n+1 < N粒子，i从n+1到N-n变化。聚类位于s（i）的局部最小值出现的地方，而局部最大值用作将路径划分为包含聚类的子集的分隔符。最小的后处理允许根据用户定义的标准去除离群值并识别明确定义的聚类。该方法的优点是，它不需要预先确定的输入或“聚类”的标准，如对象的数量或聚集体的大小。在众多可能的应用中的方法，结果在此报道，从分子动力学模拟的二元混合物的Lennard-Jones流体和模型聚合物系统与气体状物质分散在it. The方法示出允许提取有意义的定量信息的倾向分子簇或去簇和动态的聚类过程。
英文摘要：
A novel method, termed Reduced Dimensionality Cluster Identification, RDCI, is presented, for the identification and quantitative description of clusters formed by N objects in three dimensional space. The method consists of finding a path, as short as possible, connecting the objects, and then tracking down the size s, of a subgroup i-n, i-n+1, ..., i+n, of 2n+1 < N particles for i varying from n+1 to N-n. Clusters are located where local minima of s(i) occur whereas local maxima serve as delimiters partitioning the path in subsets containing the clusters. Minimal post-processing allows for the removal of outliers on the basis of user-defined criteria and the identification of clearly defined clusters. The advantage of the method is that it requires no predetermined input or criteria of "clusterness" such as number of objects or size of aggregates. Among the numerous possible applications of the method, results are herein reported, from Molecular Dynamics simulations of a binary mixture of Lennard-Jones fluids and of a model polymeric system with a gas-like substance dispersed in it. The method is shown to allow the extraction of meaningful quantitative information regarding the tendency of molecules to cluster or de-cluster and the dynamics of the clustering processes.
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