Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.csda.2009.12.008 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
simple curve alignment,aligns curve,phase variability,k-mean alignment,functional data analysis,proposed procedure,simple curve clustering,k -mean algorithm,clustering structure,curve clustering,decouples amplitude,curve alignment,novel algorithm,amplitude cluster,k,k means algorithm,k means,biological clock | Econometrics,Functional data analysis,k-means clustering,Cluster (physics),CURE data clustering algorithm,Correlation clustering,Algorithm,Linear discriminant analysis,Cluster analysis,Statistics,Amplitude,Mathematics | Journal |
Volume | Issue | ISSN |
54 | 5 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
25 | 1.83 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laura M. Sangalli | 1 | 43 | 5.37 |
| Piercesare Secchi | 2 | 70 | 11.12 |
| Simone Vantini | 3 | 60 | 9.26 |
| Valeria Vitelli | 4 | 72 | 6.93 |