Functional clustering and identifying substructures of longitudinal data
AbstractA 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, the"k"-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. Copyright 2007 Royal Statistical Society.
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Bibliographic InfoArticle provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series B (Statistical Methodology).
Volume (Year): 69 (2007)
Issue (Month): 4 ()
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- Alonso, Andrés M. & Casado, David & Romo, Juan, 2012. "Supervised classification for functional data: A weighted distance approach," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 56(7), pages 2334-2346.
- Liu, Xueli & Yang, Mark C.K., 2009. "Simultaneous curve registration and clustering for functional data," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 53(4), pages 1361-1376, February.
- Allou Samé & Faicel Chamroukhi & Gérard Govaert & Patrice Aknin, 2011. "Model-based clustering and segmentation of time series with changes in regime," Advances in Data Analysis and Classification, Springer, Springer, vol. 5(4), pages 301-321, December.
- D. S. Poskitt & Arivalzahan Sengarapillai, 2009.
"Description Length and Dimensionality Reduction in Functional Data Analysis,"
Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics
13/09, Monash University, Department of Econometrics and Business Statistics.
- Poskitt, D.S. & Sengarapillai, Arivalzahan, 2013. "Description length and dimensionality reduction in functional data analysis," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 58(C), pages 98-113.
- Slaets, Leen & Claeskens, Gerda & Hubert, Mia, 2012. "Phase and amplitude-based clustering for functional data," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 56(7), pages 2360-2374.
- Jacques, Julien & Preda, Cristian, 2014. "Model-based clustering for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 71(C), pages 92-106.
- Haeran Cho & Yannig Goude & Xavier Brossat & Qiwei Yao, 2013. "Modeling and forecasting daily electricity load curves: a hybrid approach," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library 49634, London School of Economics and Political Science, LSE Library.
- Wang, Guochang & Lin, Nan & Zhang, Baoxue, 2014. "Functional k-means inverse regression," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 70(C), pages 172-182.
- Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 55(6), pages 2090-2103, June.
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