stream (version 1.3-2)

DSD_Gaussians: Mixture of Gaussians Data Stream Generator

Description

A data stream generator that produces a data stream with a mixture of static Gaussians.

Usage

DSD_Gaussians(k=2, d=2, mu, sigma, p, separation=0.2, noise=0, noise_range)

Arguments

k

Determines the number of clusters.

d

Determines the number of dimensions.

mu

A matrix of means for each dimension of each cluster.

sigma

A list of length k of covariance matrices.

p

A vector of probabilities that determines the likelihood of generated a data point from a particular cluster.

separation

Minimum distance between cluster centers to reduce overlap between clusters (0-.8).

noise

Noise probability between 0 and 1. Noise is uniformly distributed within noise range (see below).

noise_range

A matrix with d rows and 2 columns. The first column contains the minimum values and the second column contains the maximum values for noise.

Value

Returns a DSD_Gaussians object (subclass of DSD_R, DSD) which is a list of the defined params. The params are either passed in from the function or created internally. They include:

description

A brief description of the DSD object.

k

The number of clusters.

d

The number of dimensions.

mu

The matrix of means of the dimensions in each cluster.

sigma

The covariance matrix.

p

The probability vector for the clusters.

noise

A flag that determines if or if not noise is generated.

Details

DSD_Gaussians creates a mixture of k d-dimensional static Gaussians in approximately unit space. The centers mu and the covariance matrices sigma can be supplied or will be randomly generates. The probability vector p defines for each cluster the probability that the next data point will be chosen from it (defaults to equal probability).

The generation method is similar to the one suggested by Jain and Dubes (1988).

References

Jain and Dubes(1988) Algorithms for clustering data, Prentice-Hall, Inc., Upper Saddle River, NJ, USA.

See Also

DSD

Examples

# NOT RUN {
# create data stream with three clusters in 3-dimensional data space
stream1 <- DSD_Gaussians(k=3, d=3)

plot(stream1)


# create data stream with specified clusterpositions, 
# 20% noise in a given bounding box and
# with different densities (1 to 9 between the two clusters) 
stream2 <- DSD_Gaussians(k=2, d=2, 
    mu=rbind(c(-.5,-.5), c(.5,.5)), 
    noise=0.2, noise_range=rbind(c(-1,1),c(-1,1)),
    p=c(.1,.9))
plot(stream2)
# }