streamMOA (version 1.3-0)

DSD_RandomRBFGeneratorEvents: Random RBF Generator Events Data Stream Generator

Description

A class that generates random data based on RandomRBFGeneratorEvents implemented in MOA.

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
)

Value

An object of class DSD_RandomRBFGeneratorEvent (subclass of DSD_MOA, DSD).

Arguments

k

The average number of centroids in the model.

d

The dimensionality of the data.

numClusterRange

Range for number of clusters.

kernelRadius

The average radius of the micro-clusters.

kernelRadiusRange

Deviation of the number of centroids in the model.

densityRange

Density range.

speed

Kernels move a predefined distance of 0.01 every X points.

speedRange

Speed/Velocity point offset.

noiseLevel

Noise level.

noiseInCluster

Allow noise to be placed within a cluster.

eventFrequency

Frequency of events.

eventMergeSplitOption

Merge and split?

eventDeleteCreate

Delete and create?

modelSeed

Random seed for the model.

instanceSeed

Random seed for the instances.

Author

Michael Hahsler and John Forrest

Details

There are an assortment of parameters available for the underlying MOA data structure, however, we have currently limited the available parameters to the arguments above. Currently the modelSeed and instanceSeed are set to default values every time a DSD_MOA is created, therefore the generated data will be the same. Because of this, it is important to set the seed manually when different data is needed.

The default behavior is to create a data stream with 3 clusters and concept drift. The locations of the clusters will change slightly, and they will merge with one another as time progresses.

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))
}