论文标题
限制线性网络的聚类
Clustering constrained on linear networks
论文作者
论文摘要
提出了一种无监督的分类方法，用于在线路网络上发生的点事件。这个想法依赖于随机分区模型的分布灵活性和实用性来发现聚类结构，这些聚类结构具有从特定现象中发生的观察结果，这些现象发生在给定的边缘上。通过将空间效应纳入由Dirichlet过程引起的随机分区分布中，可以控制边缘和事件之间的距离，从而导致吸引人的聚类方法。提出了Gibbs采样器算法并通过灵敏度分析评估并评估。该提案通过墨西哥城的犯罪和暴力模式的分析来激发和说明。
An unsupervised classification method for point events occurring on a network of lines is proposed. The idea relies on the distributional flexibility and practicality of random partition models to discover the clustering structure featuring observations from a particular phenomenon taking place on a given set of edges. By incorporating the spatial effect in the random partition distribution, induced by a Dirichlet process, one is able to control the distance between edges and events, thus leading to an appealing clustering method. A Gibbs sampler algorithm is proposed and evaluated with a sensitivity analysis. The proposal is motivated and illustrated by the analysis of crime and violence patterns in Mexico City.
