出版日期：2017-06-23 00:00:00

著者：Pai-Ling Li; Ling-Cheng Kuo

會議名稱：第二十六屆南區統計研討會

會議地點：國立臺北大學三峽校區商學大樓

摘要：We propose a multivariate k-centers functional clustering algorithm for the multivariate functional data. We assume that clusters can be defined via functional principal components subspace projection for each variable. A newly observed subject with multivariate functions is classified into a best-predicted cluster by minimizing a weighted distance measure, which is a weighted sum of discrepancies in observed functions and their corresponding projections onto the subspaces for all variables, among all the clusters. The weight of the proposed algorithm is flexible and can be chosen by the objective of clustering. The proposed method can take the means and modes of variation differentials among groups of each variable into account simultaneously. Numerical performance of the proposed method is demonstrated by simulation studies, with an application to a data example.

關鍵字：cluster analysis;functional principal components analysis;multivariate functional data

語言：zh_TW

會議性質：國內

校內研討會地點：無

研討會時間：20170623~20170624

通訊作者：Pai-Ling Li

國別：TWN

出處：無