FADPclust: Functional Data Clustering Using Adaptive Density Peak Detection
An implementation of a clustering algorithm for functional data based on adaptive density peak detection technique, in which the density is estimated by functional k-nearest neighbor density estimation based on a proposed semi-metric between functions. The proposed functional data clustering algorithm is computationally fast since it does not need iterative process. (Alex Rodriguez and Alessandro Laio (2014) <doi:10.1126/science.1242072>; Xiao-Feng Wang and Yifan Xu (2016) <doi:10.1177/0962280215609948>).
| Version: |
1.1.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
MFPCA, cluster, fpc, fda, fda.usc, funData, stats, graphics |
| Published: |
2022-11-07 |
| Author: |
Rui Ren [aut, cre],
Kuangnan Fang [aut],
Qingzhao Zhang [aut],
Xiaofeng Wang [aut] |
| Maintainer: |
Rui Ren <xmurr at stu.xmu.edu.cn> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| CRAN checks: |
FADPclust results |
Documentation:
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