Clustering for time-varying relational count data
Clustering for time-varying relational count data
复制标题
时变关系计数数据的聚类
DOI：
10.1016/j.csda.2020.107123
复制
发表时间：
2021
影响因子：
1.8
通讯作者：
Yadohisa Hiroshi
中科院分区：
文献类型：
--
作者：
Goto Satoshi;Takagishi Mariko;Yadohisa Hiroshi
关系计数数据通常从在线商店中的同时购买和社交网络服务信息等来源获得。对这样的关系计数数据进行聚类揭示了对象（如家庭物品或人）之间关系的潜在结构。当在多个时间点观察到的关系计数数据可用时，值得将时间结构合并到聚类结果中，以了解对象如何随时间在聚类之间移动。在本文中，我们提出了两种聚类方法来分析时变关系计数数据。第一个模型，动态泊松无限关系模型（dPSTOM），处理随时间变化的关系计数数据。在第二个模型中，我们称之为动态零膨胀泊松无限关系模型，我们进一步扩展了dPoisson，使其可以处理零膨胀数据。提出这两个模型是很重要的，因为零膨胀的数据经常遇到，特别是当时间间隔很短。此外，通过显式地推导相关的全条件分布，我们描述了估计参数的特征，进而描述了两个模型之间的关系。我们通过仿真研究和一个真实的数据例子来证明这两个模型的有效性。
Relational count data are often obtained from sources such as simultaneous purchase in online shops and social networking service information. Clustering such relational count data reveals the latent structure of the relationship between objects such as household items or people. When relational count data observed at multiple time points are available, it is worthwhile incorporating the time structure into the clustering result to understand how objects move between the clusters over time. In this paper, we propose two clustering methods for analyzing time-varying relational count data. The first model, the dynamic Poisson infinite relational model (dPIRM), handles time-varying relational count data. In the second model, which we call the dynamic zero-inflated Poisson infinite relational model, we further extend the dPIRM so that it can handle zero-inflated data. Proposing both two models is important as zero-inflated data are often encountered, especially when the time intervals are short. In addition, by explicitly deriving the relevant full conditional distributions, we describe the features of the estimated parameters and, in turn, the relationship between the two models. We show the effectiveness of both models through a simulation study and a real data example.
用于具有多余零的计数时间序列的隐马尔可夫模型
DOI：
--
发表时间：
2012
期刊：
The European Symposium on Artificial Neural Networks
影响因子：
--
作者：
Madalina Olteanu;James Ridgway
通讯作者：
James Ridgway
零膨胀幂级数分布中多余零点的贝叶斯检验
DOI：
10.1214/193940307000000068
发表时间：
2008
期刊：
arXiv: Statistics Theory
影响因子：
--
作者：
A. Bhattacharya;B. Clarke;G. Datta
通讯作者：
G. Datta