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Use an *offline macro clustering algorithm to recluster micro-clusters into a final clusters.
recluster(macro, micro, type = "auto", ...)# S3 method for DSC_Macro
recluster(macro, micro, type = "auto", ...)
The object macro is altered in place and contains the clustering.
an empty DSC_Macro.
an updated DSC_Micro with micro-clusters.
controls which clustering is used from micro. Typically
auto.
additional arguments passed on.
Michael Hahsler
Takes centers and weights of the micro-clusters and applies the macro clustering algorithm.
See DSC_TwoStage for a convenient combination of micro and macro clustering.
Other DSC:
DSC_Macro(),
DSC_Micro(),
DSC_R(),
DSC_SlidingWindow(),
DSC_Static(),
DSC_TwoStage(),
DSC(),
animate_cluster(),
evaluate.DSC,
get_assignment(),
plot.DSC(),
predict(),
prune_clusters(),
read_saveDSC
set.seed(0)
### create a data stream and a micro-clustering
stream <- DSD_Gaussians(k = 3, d = 3)
### sample can be seen as a simple online clusterer where the sample points
### are the micro clusters.
sample <- DSC_Sample(k = 50)
update(sample, stream, 500)
sample
### recluster using k-means
kmeans <- DSC_Kmeans(k = 3)
recluster(kmeans, sample)
### plot clustering
plot(kmeans, stream, type = "both", main = "Macro-clusters (Sampling + k-means)")
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