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(skmeans) Spatiotemporal clustering resampling via CLUTO

Source: R/ResamplingSptCVCluto.R
mlr_resamplings_sptcv_cluto.Rd

Spatiotemporal cluster partitioning via the vcluster executable of the CLUTO clustering application.

This partitioning method relies on the external CLUTO library. To use it, CLUTO's executables need to be downloaded and installed into this package.

See https://gist.github.com/pat-s/6430470cf817050e27d26c43c0e9be72 for an installation approach that should work on Windows and Linux. macOS is not supported by CLUTO.

Before using this method, please check the restrictive copyright shown below.

Details

By default, -clmethod='direct' is passed to the vcluster executable in contrast to the upstream default -clmethod='rb'. There is no evidence or research that this method is the best among the available ones ("rb", "rbr", "direct", "agglo", "graph", "bagglo"). Also, various other parameters can be set via argument cluto_parameters to achieve different clustering results.

Parameter -clusterfile is handled by skmeans and cannot be changed.

CLUTO's copyright is as follows:

The CLUTO package is copyrighted by the Regents of the University of Minnesota. It can be freely used for educational and research purposes by non-profit institutions and US government agencies only. Other organizations are allowed to use CLUTO only for evaluation purposes, and any further uses will require prior approval. The software may not be sold or redistributed without prior approval. One may make copies of the software for their use provided that the copies, are not sold or distributed, are used under the same terms and conditions. As unestablished research software, this code is provided on an “as is” basis without warranty of any kind, either expressed or implied. The downloading, or executing any part of this software constitutes an implicit agreement to these terms. These terms and conditions are subject to change at any time without prior notice.

References

Zhao Y, Karypis G (2002). “Evaluation of Hierarchical Clustering Algorithms for Document Datasets.” 11th Conference of Information and Knowledge Management (CIKM), 51-524. doi:10.1145/584792.584877 .

Super class

mlr3::Resampling -> ResamplingSptCVCluto

Public fields

clmethod

character
Name of the clustering method to use within vcluster. See Details for more information.

cluto_parameters

character
Additional parameters to pass to vcluster. Must be given as a single character string, e.g. "param1='value1'param2='value2'". See the CLUTO documentation for a full list of supported parameters.

verbose

logical
Whether to show vcluster progress and summary output.

Active bindings

iters

integer(1)
Returns the number of resampling iterations, depending on the values stored in the param_set.

Methods

Public methods

Inherited methods

Method new()

Create an repeated resampling instance using the CLUTO algorithm.

Usage

ResamplingSptCVCluto$new(
  id = "sptcv_cluto",
  clmethod = "direct",
  cluto_parameters = NULL,
  verbose = TRUE
)

Arguments

id

character(1)
Identifier for the resampling strategy.

clmethod

character
Name of the clustering method to use within vcluster. See Details for more information.

cluto_parameters

character
Additional parameters to pass to vcluster. Must be given as a single character string, e.g. "param1='value1'param2='value2'". See the CLUTO documentation for a full list of supported parameters.

verbose

logical
Whether to show vcluster progress and summary output.

time_var

character
The name of the variable which represents the time dimension. Must be of type numeric.

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSptCVCluto$instantiate(task)

Arguments

task

Task
A task to instantiate.

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSptCVCluto$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) {
if (mlr3misc::require_namespaces("skmeans", quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("cookfarm_mlr3")
  task$set_col_roles("Date", "time")

  # Instantiate Resampling
  rcv = rsmp("sptcv_cluto", folds = 5)
  rcv$instantiate(task)

  # Individual sets:
  rcv$train_set(1)
  rcv$test_set(1)
  # check that no obs are in both sets
  intersect(rcv$train_set(1), rcv$test_set(1)) # good!

  # Internal storage:
  rcv$instance # table
}
}