sits_dendrogram: Cluster a set of time series using hierarchical clustering

In luizassis/sits: Satellite Image Time Series Analysis

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, such that an ordered sequence of groupings is obtained. 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 (taken from the DTWCLUST package docs).

Usage

sits_dendrogram(data.tb, bands = NULL, dist_method = "dtw_basic",
  grouping_method = "ward.D2", ...)

Arguments

data.tb	a tibble the list of time series to be clustered
bands	a vector the bands to be clusterized.
dist_method	A supported distance from proxy's dist, e.g. TWDTW.
grouping_method	the agglomeration method to be used. Any ‘hclust' method (see 'hclust') Default is ’ward.D2'..
...	any additional parameters to be passed to dtwclust::tsclust() function

Value

clusters a clusters obj from dtwclust with the full dendrogram tree for data analysis

Author(s)

Gilberto Camara, [e-mail]

Rolf Simoes, [e-mail]

References

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

luizassis/sits documentation built on May 30, 2019, 7:15 p.m.