期刊
SCANDINAVIAN JOURNAL OF STATISTICS
卷 30, 期 3, 页码 581-595出版社
BLACKWELL PUBL LTD
DOI: 10.1111/1467-9469.00350
关键词
B-splines; clustering; epi-convergence; functional data; k-means; partitioning
Data in many different fields come to practitioners through a process naturally described as functional. Although data are gathered as finite vector and may contain measurement errors, the functional form have to be taken into account. We propose a clustering procedure of such data emphasizing the functional nature of the objects. The new clustering method consists of two stages: fitting the functional data by B-sptines and partitioning the estimated model coefficients using a k-means algorithm. Strong consistency of the clustering method is proved and a real-world example from food industry is given.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据