DSC_Micro: Abstract Class for Micro Clusterers (Online Component)

In mhahsler/stream: Infrastructure for Data Stream Mining

Abstract Class for Micro Clusterers (Online Component)

Description

Abstract class for all clustering methods that can operate online and result in a set of micro-clusters.

Usage

DSC_Micro(...)

Arguments

...	further arguments.

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.

Author(s)

Michael Hahsler

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
nclusters(dstream)
plot(dstream, stream, main = "micro-clusters")

mhahsler/stream documentation built on June 15, 2025, 9:45 a.m.