DSD_MG: DSD Moving Generator

In stream: Infrastructure for Data Stream Mining

DSD Moving Generator

Description

Creates an evolving DSD that consists of several MGC, each representing a moving cluster.

Usage

DSD_MG(dimension = 2, ..., labels = NULL, description = NULL)

add_cluster(x, c, label = NULL)

get_clusters(x)

remove_cluster(x, i)

## S3 method for class 'DSD_MG'
add_cluster(x, c, label = NULL)

Arguments

dimension	the dimension of the DSD object
...	initial set of MGCs
description	An optional string used by print() to describe the data generator.
x	A DSD_MG object.
c	The cluster that should be added to the DSD_MG object.
label, labels	integer representing the cluster label. NA represents noise. If labels are not specified, then each new cluster gets a new label.
i	The index of the cluster that should be removed from the DSD_MG object.

Details

This DSD is able to generate complex datasets that are able to evolve over a period of time. Its behavior is determined by as set of MGCs, each representing a moving cluster.

Author(s)

Matthew Bolanos

See Also

MGC for types of moving clusters.

Other DSD: DSD(), DSD_BarsAndGaussians(), DSD_Benchmark(), DSD_Cubes(), DSD_Gaussians(), DSD_Memory(), DSD_Mixture(), DSD_NULL(), DSD_ReadDB(), DSD_ReadStream(), DSD_Target(), DSD_UniformNoise(), DSD_mlbenchData(), DSD_mlbenchGenerator(), DSF(), animate_data(), close_stream(), get_points(), plot.DSD(), reset_stream()

Examples

### create an empty DSD_MG
stream <- DSD_MG(dim = 2)
stream

### add two clusters
c1 <- MGC_Random(density = 50, center = c(50, 50), parameter = 1)
add_cluster(stream, c1)
stream

c2 <- MGC_Noise(density = 1, range = rbind(c(-20, 120), c(-20, 120)))
add_cluster(stream, c2)
stream

get_clusters(stream)
get_points(stream, n = 5)
plot(stream, xlim = c(-20,120), ylim = c(-20, 120))

if (interactive()) {
animate_data(stream, n = 5000, xlim = c(-20, 120), ylim = c(-20, 120))
}

### remove cluster 1
remove_cluster(stream, 1)
stream

get_clusters(stream)
plot(stream, xlim = c(-20, 120), ylim = c(-20, 120))

### create a more complicated cluster structure (using 2 clusters with the same
### label to form an L shape)
stream <- DSD_MG(dim = 2,
  MGC_Static(density = 10, center = c(.5, .2),   par = c(.4, .2),   shape = Shape_Block),
  MGC_Static(density = 10, center = c(.6, .5),   par = c(.2, .4),   shape = Shape_Block),
  MGC_Static(density = 5,  center = c(.39, .53), par = c(.16, .35), shape = Shape_Block),
  MGC_Noise( density = 1,  range = rbind(c(0,1), c(0,1))),
  labels = c(1, 1, 2, NA)
  )
stream

plot(stream, xlim = c(0, 1), ylim = c(0, 1))

### simulate the clustering of a splitting cluster
c1 <- MGC_Linear(dim = 2, keyframelist = list(
  keyframe(time = 1,  dens = 20, center = c(0,0),   param = 10),
  keyframe(time = 50, dens = 10, center = c(50,50), param = 10),
  keyframe(time = 100,dens = 10, center = c(50,100),param = 10)
))

### Note: Second cluster appearch at time=50
c2 <- MGC_Linear(dim = 2, keyframelist = list(
  keyframe(time = 50, dens = 10, center = c(50,50), param = 10),
  keyframe(time = 100,dens = 10, center = c(100,50),param = 10)
))

stream <- DSD_MG(dim = 2, c1, c2)
stream

dbstream <- DSC_DBSTREAM(r = 20, lambda = 0.1)
if (interactive()) {
purity <- animate_cluster(dbstream, stream, n = 2500, type = "micro",
  xlim = c(-10, 120), ylim = c(-10, 120), measure = "purity", horizon = 100)
}

stream documentation built on May 29, 2024, 9:43 a.m.