Plot Results of a Data Stream Clustering

Method to plot the result of data stream data clustering. To plot DSD see plot.DSD().

Usage

# S3 method for class 'DSC'
plot(
  x,
  dsd = NULL,
  n = 500,
  col_points = NULL,
  col_clusters = c("red", "blue", "green"),
  weights = TRUE,
  scale = c(1, 5),
  cex = 1,
  pch = NULL,
  method = c("pairs", "scatter", "pca"),
  dim = NULL,
  type = c("auto", "micro", "macro", "both", "none"),
  assignment = FALSE,
  transform = NULL,
  ...
)

Arguments

x

the DSC object to be plotted.

dsd

a DSD object to plot the data in the background.

n

number of plots taken from dsd to plot.

col_points, col_clusters

colors used for plotting.

weights

if TRUE then the cluster weight is used for symbol size. Alternatively, a vector with the size of the symbols for micro- and macro-clusters can be supplied.

scale

range for the symbol sizes used.

cex

size factor for symbols.

pch

symbol type for points.

method

method used for plotting: "pairs" (pairs plot), "scatter" (scatter plot), "pca" (plot first 2 principal components).

dim

an integer vector with the dimensions to plot. If NULL then for methods pairs and "pca" all dimensions are used and for "scatter" the first two dimensions are plotted.

type

Plot micro clusters (type = "micro"), macro clusters (type = "macro"), both micro and macro clusters (type = "both").

assignment

logical; show assignment area of micro-clusters.

transform

a function that maps data stream points onto a 2-D plane for plotting.

...

further arguments are passed on to graphics::plot.default() or graphics::pairs(). graphics.

See also

Other DSC: DSC(), DSC_Macro(), DSC_Micro(), DSC_R(), DSC_SlidingWindow(), DSC_Static(), DSC_TwoStage(), animate_cluster(), evaluate.DSC, get_assignment(), predict, prune_clusters(), read_saveDSC, recluster()

Other plot: animate_cluster(), animate_data(), plot.DSD()

Author

Michael Hahsler

Examples

stream <- DSD_Gaussians(k = 3, d = 3, noise = 0.05)

## Example 1: Plot data
plot(stream)


## Example 2: Plot a clustering
dstream <- DSC_DStream(gridsize = 0.1)
update(dstream, stream, 500)
dstream
#> D-Stream 
#> Class: DSC_DStream, DSC_Micro, DSC_R, DSC 
#> Number of micro-clusters: 32 
#> Number of macro-clusters: 3 
plot(dstream, stream)


## plot micro or macro-clusters only
plot(dstream, stream, type = "micro")

plot(dstream, stream, type = "macro")


## plot projected on the first two principal components
## and on dimensions 2 and 3
plot(dstream, stream, method = "pca")

plot(dstream, stream, dim = c(2, 3))


## D-Stream has a special implementation to show assignment areas
plot(dstream, stream, assignment = TRUE)


## Example 4: Use a custom transformation for plotting.
##     We fit PCA using 100 points and create a transformation
##     function to project the stream to the first two PCs.
pr <- princomp(get_points(stream, n = 100, info = FALSE))
trans <- function(x) predict(pr, x)[, 1:2 , drop = FALSE]

trans(get_points(stream, n = 3))
#>           Comp.1     Comp.2
#> [1,]  0.02181325  0.3716543
#> [2,]  0.12286374  0.2548326
#> [3,] -0.07923565 -0.4639676

plot(dstream, stream, transform = trans)