dtwclust (version 6.0.0)

compare_clusterings_configs: Create clustering configurations.

Description

Create configurations for compare_clusterings()

Usage

compare_clusterings_configs(
  types = c("p", "h", "f"),
  k = 2L,
  controls = NULL,
  preprocs = pdc_configs("preproc", none = list()),
  distances = pdc_configs("distance", dtw_basic = list()),
  centroids = pdc_configs("centroid", default = list()),
  no.expand = character(0L)
)

Value

A list for each clustering type, each of which includes a data frame with the computed and merged configurations. Each data frame has an extra attribute num.configs specifying the number of configurations.

Arguments

types

Clustering types. It must be any combination of (possibly abbreviated): partitional, hierarchical, fuzzy, tadpole.

k

A numeric vector with one or more elements specifying the number of clusters to test.

controls

A named list of tsclust-controls. NULL means defaults. See details.

preprocs

Preprocessing configurations. See details.

distances

Distance configurations. See details.

centroids

Centroid configurations. See details.

no.expand

A character vector indicating parameters that should not be expanded between pdc_configs() configurations. See examples.

Details

Preprocessing, distance and centroid configurations are specified with the helper function pdc_configs(), refer to the examples in compare_clusterings() to see how this is used.

The controls list may be specified with the usual tsclust-controls functions. The names of the list must correspond to "partitional", "hierarchical", "fuzzy" or "tadpole" clustering. Again, please refer to the examples in compare_clusterings().

Examples

# compare this with leaving no.expand empty
compare_clusterings_configs(
    distances = pdc_configs("d", dtw_basic = list(window.size = 1L:2L, norm = c("L1", "L2"))),
    centroids = pdc_configs("c", dba = list(window.size = 1L:2L, norm = c("L1", "L2"))),
    no.expand = c("window.size", "norm")
)