用于实时数据分析的本征正交分解方法：二手烟暴露干预中的峰值聚类探索
Proper Orthogonal Decomposition Methods for the Analysis of Real-Time Data: Exploring Peak Clustering in a Secondhand Smoke Exposure Intervention.
作者信息
Berardi V, Carretero-González R, Klepeis N E, Palacios A, Bellettiere J, Hughes S, Obayashi S, Hovell M F
机构信息
Nonlinear Dynamical Systems Group, Computational Science Research Center, and Department of Mathematics and Statistics, San Diego State University, San Diego, California 92182-7720, USA.
Center for Behavioral Epidemiology and Community Health, Graduate School of Public Health, San Diego State University, San Diego, California 92182-7720, USA.
出版信息
J Comput Sci. 2015 Nov;11:102-111. doi: 10.1016/j.jocs.2015.10.006. Epub 2015 Oct 19.
This work explores a method for classifying peaks appearing within a data-intensive time-series. We summarize a case study from a clinical trial aimed at reducing secondhand smoke exposure via the installation of air particle monitors in households. Proper orthogonal decomposition (POD) in conjunction with a -means clustering algorithm assigns each data peak to one of two clusters. Aversive feedback from the monitors increased the proportion of short-duration, attenuated peaks from 38.8% to 96.6%. For each cluster, a distribution of parameters from a physics-based model of airborne particles is estimated. Peaks generated from these distributions are correctly identified by POD/clustering with >60% accuracy.
这项工作探索了一种对数据密集型时间序列中出现的峰值进行分类的方法。我们总结了一项临床试验的案例研究，该试验旨在通过在家庭中安装空气颗粒物监测器来减少二手烟暴露。适当正交分解（POD）与k均值聚类算法相结合，将每个数据峰值分配到两个聚类之一。监测器的厌恶反馈使短持续时间、衰减峰值的比例从38.8%增加到96.6%。对于每个聚类，估计了基于物理的空气传播颗粒模型的参数分布。通过POD/聚类正确识别了这些分布产生的峰值，准确率超过60%。
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