View source: R/DSC_SlidingWindow.R
DSC_SlidingWindow | R Documentation |
The clusterer keeps a sliding window for the stream and rebuilds a DSC clustering model at regular intervals. By default is uses DSC_Kmeans. Other DSC_Macro clusterer can be used.
DSC_SlidingWindow(formula = NULL, model = DSC_Kmeans, window, rebuild, ...)
formula |
a formula for the classification problem. |
model |
regression model (that has a formula interface). |
window |
size of the sliding window. |
rebuild |
interval (number of points) for rebuilding the regression. Set rebuild to
|
... |
additional parameters are passed on to the clusterer (default is DSC_Kmeans). |
This constructor creates a clusterer based on DST_SlidingWindow
. The clusterer has
a update()
and predict()
method.
The difference to setting up a DSC_TwoStage is that DSC_SlidingWindow
rebuilds
the model in regular intervals, while DSC_TwoStage
rebuilds the model on demand.
An object of class DST_SlidingWindow
.
Michael Hahsler
Other DSC:
DSC_Macro()
,
DSC_Micro()
,
DSC_R()
,
DSC_Static()
,
DSC_TwoStage()
,
DSC()
,
animate_cluster()
,
evaluate.DSC
,
get_assignment()
,
plot.DSC()
,
predict()
,
prune_clusters()
,
read_saveDSC
,
recluster()
Other DSC_Macro:
DSC_DBSCAN()
,
DSC_EA()
,
DSC_Hierarchical()
,
DSC_Kmeans()
,
DSC_Macro()
,
DSC_Reachability()
library(stream) stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05) # define the stream clusterer. cl <- DSC_SlidingWindow( formula = ~ . - `.class`, k = 3, window = 50, rebuild = 10 ) cl # update the clusterer with 100 points from the stream update(cl, stream, 100) # get the cluster model cl$model$result plot(cl$model$result)
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