sits_cluster_dendro | R Documentation |
These functions support hierarchical agglomerative clustering in sits. They provide support from creating a dendrogram and using it for cleaning samples.
link[sits]{sits_cluster_dendro()}
takes a tibble with time series and
produces a sits tibble with an added "cluster" column. The function first
calculates a dendrogram and obtains a validity index for best clustering
using the adjusted Rand Index. After cutting the dendrogram using the chosen
validity index, it assigns a cluster to each sample.
link[sits]{sits_cluster_frequency()}
computes the contingency
table between labels
and clusters and produces a matrix.
Its input is a tibble produced by link[sits]{sits_cluster_dendro()}
.
link[sits]{sits_cluster_clean()}
takes a tibble with time series
that has an additional 'cluster' produced by
link[sits]{sits_cluster_dendro()}
and removes labels that are minority in each cluster.
sits_cluster_dendro(
samples,
bands = NULL,
dist_method = "dtw_basic",
linkage = "ward.D2",
k = NULL,
palette = "RdYlGn"
)
## S3 method for class 'sits'
sits_cluster_dendro(
samples,
bands = NULL,
dist_method = "dtw_basic",
linkage = "ward.D2",
k = NULL,
palette = "RdYlGn",
...
)
## Default S3 method:
sits_cluster_dendro(samples, ...)
samples |
Tibble with input set of time series (class "sits"). |
bands |
Bands to be used in the clustering (character vector) |
dist_method |
One of the supported distances (single char vector) "dtw": DTW with a Sakoe-Chiba constraint. "dtw2": DTW with L2 norm and Sakoe-Chiba constraint. "dtw_basic": A faster DTW with less functionality. "lbk": Keogh's lower bound for DTW. "lbi": Lemire's lower bound for DTW. |
linkage |
Agglomeration method to be used (single char vector) One of "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median" or "centroid". |
k |
Desired number of clusters (overrides default value) |
palette |
Color palette as per 'grDevices::hcl.pals()' function. |
... |
Additional parameters to be passed to dtwclust::tsclust() function. |
Tibble with "cluster" column (class "sits_cluster").
Please refer to the sits documentation available in <https://e-sensing.github.io/sitsbook/> for detailed examples.
Rolf Simoes, rolf.simoes@inpe.br
"dtwclust" package (https://CRAN.R-project.org/package=dtwclust)
if (sits_run_examples()) {
# default
clusters <- sits_cluster_dendro(cerrado_2classes)
# with parameters
clusters <- sits_cluster_dendro(cerrado_2classes,
bands = "NDVI", k = 5)
}
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