DSD_MG: DSD Moving Generator

In stream: Infrastructure for Data Stream Mining

DSD Moving Generator

Description

Creates an evolving data stream generator from a list of Moving Generator Clusters (MGCs), each representing one cluster that can be static or change over time.

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. All MGCs added to the stream need to have the same dimensionality.
...	initial set of MGCs.
description	An optional string used by print() to describe the data generator.
x	A DSD_MG object.
c	The cluster (MGC) 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. labels is a vector with one label for each cluster in ....
i	The index of the cluster that should be removed from the DSD_MG object. The index refers to the list returned by get_clusters().

Details

DSD_MG is able to generate complex data streams with known ground truth that evolve over a period of time: clusters can move, grow, shrink, appear, disappear, split or merge. The behavior of the stream is determined by its components, the MGCs. See MGC for the available components, how density and generator time work, and how to define cluster shapes.

Each point is returned with a .class column containing the label of the component that produced it. By default, each component gets a new integer label. Several components can share a label to form one ground-truth cluster (e.g., to create non-convex clusters). Noise components (MGC_Noise) get the label NA unless a label is specified.

The generator can be modified after it was created: add_cluster() adds a component, remove_cluster() removes the component with a given index in the list returned by get_clusters(). reset_stream() sets the generator time back (to 1 by default). Note that streams and components are mutable objects. Modifying a component also affects every stream that contains it.

Value

DSD_MG() returns a DSD_MG object (a DSD). add_cluster() and remove_cluster() modify and invisibly return the stream, and get_clusters() returns the list of MGC components.

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(dimension = 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(dimension = 2,
  MGC_Static(density = 10, center = c(.5, .2),   parameter = c(.4, .2),
             shape = Shape_Block),
  MGC_Static(density = 10, center = c(.6, .5),   parameter = c(.2, .4),
             shape = Shape_Block),
  MGC_Static(density = 5,  center = c(.39, .53), parameter = 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(dimension = 2, keyframelist = list(
  keyframe(time = 1,  density = 20, center = c(0,0),   parameter = 10),
  keyframe(time = 50, density = 10, center = c(50,50), parameter = 10),
  keyframe(time = 100,density = 10, center = c(50,100),parameter = 10)
))

### Note: The second cluster appears at time = 50
c2 <- MGC_Linear(dimension = 2, keyframelist = list(
  keyframe(time = 50, density = 10, center = c(50,50), parameter = 10),
  keyframe(time = 100,density = 10, center = c(100,50),parameter = 10)
))

stream <- DSD_MG(dimension = 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 Nov. 10, 2026, 5:07 p.m.