Functional clustering and identifying substructures of longitudinal data

doi:10.1111/j.1467-9868.2007.00605.x

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/20735

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題名:	Functional clustering and identifying substructures of longitudinal data
作者:	Chiou, Jeng-Min;Li, Pai-ling
貢獻者:	淡江大學統計學系
關鍵詞:	Classification;Clustering;Functional data;Functional principal component analysis;Modes of variation;Stochastic processes
日期:	2007-09-01
上傳時間:	2009-11-30 12:58:10 (UTC+8)
出版者:	Chichester: Wiley-Blackwell Publishing Ltd.
摘要:	A functional clustering (FC) method, k-centres FC, for longitudinal data is proposed. The k-centres FC approach accounts for both the means and the modes of variation differentials between clusters by predicting cluster membership with a reclassification step. The cluster membership predictions are based on a non-parametric random-effect model of the truncated Karhunen–Loève expansion, coupled with a non-parametric iterative mean and covariance updating scheme. We show that, under the identifiability conditions derived, the k-centres FC method proposed can greatly improve cluster quality as compared with conventional clustering algorithms. Moreover, by exploring the mean and covariance functions of each cluster, thek-centres FC method provides an additional insight into cluster structures which facilitates functional cluster analysis. Practical performance of the k-centres FC method is demonstrated through simulation studies and data applications including growth curve and gene expression profile data.
關聯:	Journal of the Royal Statistical Society B 69(4), pp.679-699
DOI:	10.1111/j.1467-9868.2007.00605.x
顯示於類別:	[統計學系暨研究所] 期刊論文

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