stream (version 0.1-1)

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)

Arguments

k

The number of clusters.

d

The dimensionality of the data.

numClusterRange

Number of clusters.

kernelRadius

The average radius of the micro-clusters.

kernelRadiusRange

Radius range of the micro-clusters.

densityRange

Density range.

speed

Speed.

speedRange

Speed range.

noiseLevel

Noise level.

noiseInCluster

Noise in 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.

Value

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

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

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

See Also

DSD

Examples

dsd <- DSD_RandomRBFGeneratorEvents()

animate_data(dsd, n=5000, pointInterval=100, xlim=c(0,1), ylim=c(0,1))