DSC_R: Abstract Class for Implementing R-based Clusterers

In mhahsler/stream: Infrastructure for Data Stream Mining

Abstract Class for Implementing R-based Clusterers

Description

Abstract class for implementing R-based clusterers.

Usage

DSC_R(...)

## S3 method for class 'DSC_R'
update(
  object,
  dsd,
  n = 1L,
  verbose = FALSE,
  block = 10000L,
  return = c("nothing", "assignment", "model"),
  ...
)

Arguments

...	further arguments.
object	a DSC object.
dsd	a data stream object.
n	number of data points taken from the stream.
verbose	logical; show progress?
block	process blocks of data to improve speed.
return	a character string indicating what update returns. The default is "nothing". Other possible values depend on the DST. Examples are "model" and "assignment".

Details

DSC_R cannot be instantiated.

Implementing new Classes

To implement a new clusterer you need to create an S3 class with elements description and RObj. RObj needs to be a reference class with methods:

• cluster(newdata, ...)

• get_microclusters(...)

• get_microweights(...)

• get_macroclusters(...)

• get_macroweights(...)

• microToMacro(micro, ...)

See DSC for details and parameters.

DSC_R cannot be instantiated.

Author(s)

Michael Hahsler

See Also

Other DSC: DSC(), DSC_Macro(), DSC_Micro(), DSC_SlidingWindow(), DSC_Static(), DSC_TwoStage(), animate_cluster(), evaluate.DSC, get_assignment(), plot.DSC(), predict(), prune_clusters(), read_saveDSC, recluster()

mhahsler/stream documentation built on June 15, 2025, 9:45 a.m.