diagnostic_plot

This function plots the values of the distance to center and silhouette for
each observation. Observations are ordered within cluster by decreasing value
of silhouette.

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

diagnostic_plot function

<dl><dt>x</dt>
<dd>An object of class <code>caps</code>.</dd></dl>

Arguments

Diagnostic plot for the result of a clustering strategy stored in a caps
object — diagnostic_plot

<dl>

<dt>x</dt>
<dd>An object of class <code>caps</code>.</dd>

</dl>

diagnostic_plot: Diagnostic plot for the result of a clustering strategy stored in a `caps` object

Description

Usage

Value

Arguments

Examples