R: Compute a dendrogram using hierarchical clustering

.sits_cluster_dendrogram {sits} | R Documentation

## Compute a dendrogram using hierarchical clustering

### Description

Cluster time series in hierarchical mode. Hierarchical clustering, as its name suggests, is an algorithm that tries to create a hierarchy of groups in which, as the level in the hierarchy increases, clusters are created by merging the clusters from the next lower level, producing an ordered sequence of groupings. The similarity measure used to group time series in a cluster is the dtw metric. The procedure is deterministic, so it will always give the same result for a chosen set of similarity measures (see `tsclust`).

### Usage

    .sits_cluster_dendrogram(
      samples,
      bands,
      dist_method = "dtw_basic",
      linkage = "ward.D2",
      ...
    )

### Arguments

`samples` | Time series data and metadata to be used to generate the dendrogram.
`bands` | Vector of bands to be clustered.
`dist_method` | String with one of the supported distance from proxy's dist, e.g. `TWDTW`.
`linkage` | String with agglomeration method to be used. Can be any 'hclust' method (see 'hclust'). Default is 'ward.D2'.
`...` | Any additional parameters to be passed to dtwclust::tsclust() function.

### Value

A full dendrogram tree for data analysis.

### Author(s)

Rolf Simoes, rolf.simoes@inpe.br

Gilberto Camara, gilberto.camara@inpe.br

### References

'dtwclust' package (https://CRAN.R-project.org/package=dtwclust)

[Package sits version 0.16.1 Index]