simulated30

A data set containing 30 simulated uni-dimensional curves.

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

simulated30 function

A list with abscissas x and values y:<dl>
<dt>x</dt>
<dd>Matrix 30x200;</dd><dt>y</dt>
<dd>Array 30x1x200.</dd>
</dl>

Format

Simulated data for examples — simulated30

A list with abscissas x and values y:<dl>
<dt>x</dt>
<dd>Matrix 30x200;</dd>

<dt>y</dt>
<dd>Array 30x1x200.</dd>


</dl>

simulated30: Simulated data for examples

Description

Usage

Arguments

Format