Application of Functional Data Clustering Methods on Missing Data / 世界科学技术-中医药现代化

ABSTRACT

This article mainly introduces the functional clustering methods and demonstrates its performance by the real analysis of Chinese medical Zong Qi data.The functional clustering analysis hypothesizes that the discrete time series observations are dominated by a continuous function of time,which can be expressed by infinite basis functions.Functional clustering methods include raw data method,filtering method and adaptive method.When dealing with the sparse data clustering analysis,raw data method encounters the difficulty of matrix calculation due to the lack of data on some time grids.Filtering method suits for full time data,while when facing missing data,the fitting curve is inaccurate so that the clustering outcome cannot be explainable.Adaptive method can be applied flexibly to both full time and sparsely sampled data.In the real analysis section,the adaptive method is used to cluster the sparsely sampled Chinese medical Zong Qi time series data,where the elderly individuals are divided into three clusters,the ones with high level of Zong Qi,the ones with moderate level and those with low level.The adaptive method performs well on clustering individuals.