The nested joint clustering via Dirichlet process mixture model.
The nested joint clustering via Dirichlet process mixture model.
复制标题
通过狄利克雷过程混合模型的嵌套联合聚类。
DOI：
10.1080/00949655.2019.1572756
复制
发表时间：
2019
影响因子：
1.2
通讯作者：
Arshad,Hasan
中科院分区：
文献类型：
--
作者：
Han,Shengtong;Zhang,Hongmei;Sheng,Wenhui;Arshad,Hasan
本文主要研究基于Dirichlet过程(DP)混合的聚类问题。与其他已有的聚类方法不同，该半参数模型能够同时建模时不变模式和时态模式，同时考虑了共同模式和独特模式，具有很强的灵活性。此外，通过对被试和相关变量进行联合聚类，可以捕捉到被试之间和变量之间内在的复杂共享模式。使用DP直接推断簇的数量和簇分配。仿真研究表明了该方法的有效性。讨论了风团大小数据的应用，目的是在受试者群内的过敏原中识别新的时间模式。
This article focuses on the clustering problem based on Dirichlet process (DP) mixtures. To model both time invariant and temporal patterns, different from other existing clustering methods, the proposed semi-parametric model is flexible in that both the common and unique patterns are taken into account simultaneously. Furthermore, by jointly clustering subjects and the associated variables, the intrinsic complex shared patterns among subjects and among variables are expected to be captured. The number of clusters and cluster assignments are directly inferred with the use of DP. Simulation studies illustrate the effectiveness of the proposed method. An application to wheal size data is discussed with an aim of identifying novel temporal patterns among allergens within subject clusters.