DSD_RandomRBFGeneratorEvents: Random RBF Generator Events Data Stream Generator

In streamMOA: Interface for MOA Stream Clustering Algorithms

Random RBF Generator Events Data Stream Generator

Description

Generates random data using MOA's RandomRBFGeneratorEvents stream.

Usage

DSD_RandomRBFGeneratorEvents(
  k = 3,
  d = 2,
  numClusterRange = 3L,
  kernelRadius = 0.07,
  kernelRadiusRange = 0,
  densityRange = 0,
  speed = 100L,
  speedRange = 0L,
  noiseLevel = 0.1,
  noiseInCluster = FALSE,
  eventFrequency = 30000L,
  eventMergeSplitOption = FALSE,
  eventDeleteCreate = FALSE,
  modelSeed = NULL,
  instanceSeed = NULL
)

Arguments

k	Average number of centroids in the model.
d	Number of dimensions in the generated stream.
numClusterRange	Range for the number of clusters.
kernelRadius	Average radius of the micro-clusters.
kernelRadiusRange	Range of variation in micro-cluster radii.
densityRange	Range of variation in cluster density.
speed	Number of points between kernel movements.
speedRange	Range of variation in kernel speed.
noiseLevel	Proportion of noise points.
noiseInCluster	If TRUE, allow noise points inside clusters.
eventFrequency	Number of points between concept-drift events.
eventMergeSplitOption	If TRUE, enable cluster merge and split events.
eventDeleteCreate	If TRUE, enable cluster deletion and creation events.
modelSeed	Random seed for the cluster model.
instanceSeed	Random seed for generated instances.

Details

Only a subset of the parameters supported by the underlying MOA generator is exposed. If modelSeed or instanceSeed is NULL, a seed is sampled from R's random-number generator. Set these arguments explicitly to reproduce a stream; call set.seed() to make the generated default seeds reproducible.

By default, the generator creates three clusters with concept drift. Cluster locations move over time, and clusters may merge.

Value

An object of class DSD_RandomRBFGeneratorEvents (subclass of DSD_MOA, stream::DSD).

Author(s)

Michael Hahsler and John Forrest

References

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl. MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering Journal of Machine Learning Research (JMLR), 2010.

See Also

Other DSD_MOA: DSD_MOA()

Examples

stream <- DSD_RandomRBFGeneratorEvents()
get_points(stream, 10)

if (interactive()) {
animate_data(stream, n = 5000, horizon = 100, xlim = c(0, 1), ylim = c(0, 1))
}

streamMOA documentation built on Oct. 14, 2026, 1:06 a.m.