sim30_caps

<p>An object of class <code>caps</code> storing the result of the <code>fdakmeans()</code>
function applied on the data set <code>simulated30</code> using the affine warping
class and the Pearson metric and searching for 2 clusters.</p>

datasets

Implementations of the k-means, hierarchical agglomerative and
DBSCAN clustering methods for functional data which allows for jointly
aligning and clustering curves. It supports functional data defined on
one-dimensional domains but possibly evaluating in multivariate codomains.
It supports functional data defined in arrays but also via the 'fd' and
'funData' classes for functional data defined in the 'fda' and 'funData'
packages respectively. It currently supports shift, dilation and affine
warping functions for functional data defined on the real line and uses the
SRVF framework to handle boundary-preserving warping for functional data
defined on a specific interval. Main reference for the k-means algorithm:
Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment
for curve clustering" <doi:10.1016/j.csda.2009.12.008>. Main reference for
the SRVF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013)
"Generative models for functional data using phase and amplitude separation"
<doi:10.1016/j.csda.2012.12.001>.

Aymeric Stamm

fdacluster

Joint Clustering and Alignment of Functional Data

Laura Sangalli

Piercesare Secchi

Simone Vantini

Valeria Vitelli

Alessandro Zito

sim30_caps function

<p>An object of class <code>caps</code>.</p>

Format

A caps object from simulated data for examples — sim30_caps

A <code>caps</code> object from simulated data for examples

sim30_caps: A `caps` object from simulated data for examples

Description

Usage

Arguments

Format