## Overview

The **fdacluster** package provides implementations of the popular \(k\)-means, hierarchical agglomerative and DBSCAN clustering methods for functional data (Ramsay and Silverman 2005). Variability in functional data can be divided into three components: *amplitude*, *phase* and *ancillary* variability (Vantini 2012; Marron et al. 2015). The first two sources of variability can be captured with a statistical analysis that integrates a *curve alignment* step. The \(k\)-means and HAC algorithms implemented in **fdacluster** provide clustering structures that are based either on amplitude variation (default behavior) or phase variation (Marron et al. 2014). This is achieved by jointly performing clustering and alignment of a functional data set. The three main related functions are `fdakmeans()`

for the \(k\)-means, `fdahclust()`

for HAC and `fdadbscan()`

for DBSCAN.

It supports:

- functional data defined on
*one-dimensional domains*but possibly evaluating in*multivariate codomains*; - functional data defined in arrays but also via the
`fd`

and`funData`

classes for functional data defined in the**fda**and**funData**packages respectively; - shift, dilation and affine warping functions for functional data defined on the real line (Sangalli et al. 2010) and all boundary-preserving warping functions for functional data defined on a specific interval through the SRSF framework (Tucker, Wu, and Srivastava 2013).

## Installation

You can install the released version of **fdacluster** from CRAN with:

`install.packages("fdacluster")`

Alternatively you can install the development version of **fdacluster** from GitHub with:

```
# install.packages("remotes")
::install_github("astamm/fdacluster") remotes
```

## References

*Statistical Science*, 468–84.

*Functional Data Analysis*. Springer Series in Statistics. Springer.

*Computational Statistics & Data Analysis*54 (5): 1219–33.

*Computational Statistics & Data Analysis*61: 50–66.

*Test*21 (4): 676–96. https://doi.org/10.1007/s11749-011-0268-9.