Abstract class for all clustering methods that can operate online and result in a set of micro-clusters.
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
to add new points from a data stream to the clustering. The result is
a set of micro-clusters that can be retrieved using
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.
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")
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