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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Description
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