| DSC_TwoStage {stream} | R Documentation |
TwoStage Clustering Process
Description
Combines an online clustering component (DSC_Micro) and an offline reclustering component (DSC_Macro) into a single process.
Usage
DSC_TwoStage(micro, macro)
Arguments
micro |
Clustering algorithm used in the online stage (DSC_Micro) |
macro |
Clustering algorithm used for reclustering in the offline stage (DSC_Macro) |
Details
update() runs the online micro-clustering stage and only when macro cluster
centers/weights are requested using get_centers() or get_weights(), then the offline stage
reclustering is automatically performed.
Available clustering methods can be found in the See Also section below.
Value
An object of class DSC_TwoStage (subclass of DSC,
DSC_Macro) which is a named list with elements:
-
description: a description of the clustering algorithms. -
micro: The DSD used for creating micro clusters in the online component. -
macro: The DSD for offline reclustering. -
state: an environment storing state information needed for reclustering.
with the two clusterers. The names are “
Author(s)
Michael Hahsler
See Also
Other DSC_TwoStage:
DSC_DBSTREAM(),
DSC_DStream(),
DSC_evoStream()
Other DSC:
DSC(),
DSC_Macro(),
DSC_Micro(),
DSC_R(),
DSC_SlidingWindow(),
DSC_Static(),
animate_cluster(),
evaluate.DSC,
get_assignment(),
plot.DSC(),
predict(),
prune_clusters(),
read_saveDSC,
recluster()
Examples
stream <- DSD_Gaussians(k = 3, d = 2)
# Create a clustering process that uses a window for the online stage and
# k-means for the offline stage (reclustering)
win_km <- DSC_TwoStage(
micro = DSC_Window(horizon = 100),
macro = DSC_Kmeans(k = 3)
)
win_km
update(win_km, stream, 200)
win_km
win_km$micro
win_km$macro
plot(win_km, stream)
evaluate_static(win_km, stream, assign = "macro")