Abstract class for all clustering methods that can operate online and result in a set of micro-clusters.
Details
Micro-clustering algorithms are data stream mining tasks DST
which implement the online component of data stream clustering.
The clustering is performed sequentially by using update()
to add new points from a data stream to the clustering. The result is
a set of micro-clusters that can be retrieved using get_clusters().
Available clustering methods can be found in the See Also section below.
Many data stream clustering algorithms define both, the online and an offline component to recluster micro-clusters into larger clusters called macro-clusters. This is implemented here as class DSC_TwoStage.
DSC_Micro cannot be instantiated.
See also
Other DSC_Micro:
DSC_BICO(),
DSC_BIRCH(),
DSC_DBSTREAM(),
DSC_DStream(),
DSC_Sample(),
DSC_Window(),
DSC_evoStream()
Other DSC:
DSC(),
DSC_Macro(),
DSC_R(),
DSC_SlidingWindow(),
DSC_Static(),
DSC_TwoStage(),
animate_cluster(),
evaluate.DSC,
get_assignment(),
plot.DSC(),
predict,
prune_clusters(),
read_saveDSC,
recluster()
Examples
stream <- DSD_BarsAndGaussians(noise = .05)
# Use a DStream to create micro-clusters
dstream <- DSC_DStream(gridsize = 1, Cm = 1.5)
update(dstream, stream, 1000)
dstream
#> D-Stream
#> Class: DSC_DStream, DSC_Micro, DSC_R, DSC
#> Number of micro-clusters: 44
#> Number of macro-clusters: 4
nclusters(dstream)
#> [1] 44
plot(dstream, stream, main = "micro-clusters")
