k -mean alignment for curve clustering

The problem of curve clustering when curves are misaligned is considered. A novel algorithm is described, which jointly clusters and aligns curves. The proposed procedure efficiently decouples amplitude and phase variability; in particular, it is able to detect amplitude clusters while simultaneousl...

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Bibliographic Details
Published in:	Computational statistics & data analysis Vol. 54; no. 5; pp. 1219 - 1233
Main Authors:	Sangalli, Laura M., Secchi, Piercesare, Vantini, Simone, Vitelli, Valeria
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.05.2010 Elsevier
Series:	Computational Statistics & Data Analysis
Subjects:	[formula omitted]-mean algorithm Curve alignment Curve clustering Exact sciences and technology Functional analysis Functional data analysis Functional data analysis Curve alignment Curve clustering k-mean algorithm General topics Mathematical analysis Mathematics Multivariate analysis Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Probability and statistics Sciences and techniques of general use Statistics k -mean algorithm Curve clustering Functional data analysis Curve alignment Discriminant analysis Data analysis Cluster analysis (statistics) Functional analysis k-mean algorithm Multivariate analysis Statistical simulation Algorithm Mean estimation Statistical computation
ISSN:	0167-9473, 1872-7352
Online Access:	Get full text
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Summary:	The problem of curve clustering when curves are misaligned is considered. A novel algorithm is described, which jointly clusters and aligns curves. The proposed procedure efficiently decouples amplitude and phase variability; in particular, it is able to detect amplitude clusters while simultaneously disclosing clustering structures in the phase, pointing out features that can neither be captured by simple curve clustering nor by simple curve alignment. The procedure is illustrated via simulation studies and applications to real data.
ISSN:	0167-9473 1872-7352
DOI:	10.1016/j.csda.2009.12.008