论文标题
使用多元功能主组件分析对步骤数据进行多功能聚类
Multi-feature Clustering of Step Data using Multivariate Functional Principal Component Analysis
论文作者
论文摘要
本文提出了一种用于聚类步骤数据的新统计方法，这是一种流行的健康记录数据形式，可以轻松从可穿戴设备获得。由于步骤数据是高维且充气零的，因此无法直接应用经典方法，例如K均值和围绕MEDOID（PAM）的分区。所提出的方法是新构建的变量的新型组合，反映了步骤数据的固有特征，例如数量，强度和模式，以及一个多元功能主成分分析，可以整合用于群集的步骤数据的所有特征。提出的方法是通过将常规聚类方法（例如K-均值和PAM）应用于从这些变量获得的多元功能主成分分数来实现的。仿真研究和实际数据分析表明，聚类质量的显着改善。
This paper presents a new statistical method for clustering step data, a popular form of health record data easily obtained from wearable devices. Since step data are high-dimensional and zero-inflated, classical methods such as K-means and partitioning around medoid (PAM) cannot be applied directly. The proposed method is a novel combination of newly constructed variables that reflect the inherent features of step data, such as quantity, strength, and pattern, and a multivariate functional principal component analysis that can integrate all the features of the step data for clustering. The proposed method is implemented by applying a conventional clustering method such as K-means and PAM to the multivariate functional principal component scores obtained from these variables. Simulation studies and real data analysis demonstrate significant improvement in clustering quality.
