stream (version 1.2-3)

animation: Animates the plotting of a DSD and the clustering process

Description

Generates an animation of a data stream or a data steam clustering.

Usage

animate_data(dsd, horizon = 100, n = 1000, wait = .1, plot.args = NULL, ...)
animate_cluster(dsc, dsd, measure, horizon = 100, n = 1000,  type=c("auto", "micro", "macro"), assign="micro",  assignmentMethod=c("auto","model", "nn"), noise = c("class", "ignor"), wait=.1, plot.args = NULL, ...)

Arguments

dsd

a DSD object

dsc

a DSC object

horizon

the number of points displayed at once/used for evaluation.

n

the number of points to be plotted

measure

the evaluation measure that should be graphed below the animation

type

evaluate "micro" or "macro"-clusters? "auto" chooses micro if dsc is of class DSC_micro and no macro is given. Otherwise macro is used.

assign

assign new points to the closest "micro" or "macro"-cluster to calculate the evaluation measure.

assignmentMethod

how to assign data points to micro-clusters. Options are "model" and "nn" (nearest neighbor). "auto" uses model if available and nn otherwise.

noise

how to handle noise (as a separate class or ignor).

wait

the time interval between each frame

plot.args

a list with plotting parameters for the clusters.

...

extra arguments are added to plot.args.

Details

Animations are recorded using the library animation and can be replayed (which gives a smoother experience since the is no more computation done) and saved in various formats (see Examples section below).

See Also

evaluate_cluster for stream evaluation without animation. See ani.replay for replaying and saving animations.

Examples

## Not run: 
# stream <- DSD_Benchmark(1)
# animate_data(stream, horizon=100, n=5000, xlim=c(0,1), ylim=c(0,1))
# 
# ### animations can be replayed with the animation package
# library(animation)
# animation::ani.options(interval=.1) ## change speed
# ani.replay()
#   
# ### animations can also be saved as HTML, animated gifs, etc.
# saveHTML(ani.replay())  
#   
# ### animate the clustering process with evaluation
# ### Note: we choose to ignor noise points even if the algorithm would assign 
# ###       them to a cluster
# reset_stream(stream)
# dbstream <- DSC_DBSTREAM(r=.04, lambda=.1, gaptime=100, Cm=3, 
#   shared_density=TRUE, alpha=.2)  
# 
# animate_cluster(dbstream, stream, horizon=100, n=5000,  
#   measure="crand", type="macro", assign="micro", noise = "ignor",
#   plot.args = list(xlim=c(0,1), ylim=c(0,1), shared = TRUE))
# ## End(Not run)