DSC | R Documentation |
Abstract base classes for Data Stream Clustering (DSC).
Concrete implementations are functions starting with DSC_
(RStudio use auto-completion with Tab to select one).
DSC(...) get_centers(x, type = c("auto", "micro", "macro"), ...) get_weights(x, type = c("auto", "micro", "macro"), scale = NULL, ...) get_copy(x) nclusters(x, type = c("auto", "micro", "macro"), ...) get_microclusters(x, ...) get_microweights(x, ...) get_macroclusters(x, ...) get_macroweights(x, ...)
... |
further parameter |
x |
a DSC object. |
type |
Return weights of micro- or macro-clusters in x. Auto uses the class of x to decide. |
scale |
a range (from, to) to scale the weights. Returns by default the raw weights. |
The DSC
class cannot be instantiated (calling
DSC()
produces only a message listing the available implementations),
but they serve as a base
class from which other DSC classes inherit.
Data stream clustering has typically an
online clustering component (see DSC_Micro), and an
offline reclustering component (see DSC_Macro).
Class DSC
provides several generic functions that can operate on all
DSC subclasses. See Usage and Functions sections for methods.
Additional, separately documented methods are:
update()
adds new data points from a stream to a clustering.
predict()
predicts the cluster assignment for new data points.
plot()
plots cluster centers (see plot.DSC()
).
get_centers()
and get_weights()
are typically overwritten by
subclasses of DSC
.
Since DSC
objects often contain external pointers, regular saving and
reading operations will fail. Use saveDSC()
and readDSC()
which will serialize the objects first appropriately.
get_centers()
: Gets the cluster centers (micro- or macro-clusters) from a DSC object.
get_weights()
: Get the weights of the clusters in the DSC (returns 1s if not implemented by the clusterer)
get_copy()
: Create a Deep Copy of a DSC Object that contain reference classes (e.g., Java data structures for MOA).
nclusters()
: Returns the number of micro-clusters from the DSC object.
get_microclusters()
: Used as internal interface.
get_microweights()
: Used as internal interface.
get_macroclusters()
: Used as internal interface.
get_macroweights()
: Used as internal interface.
Michael Hahsler
Other DST:
DSAggregate()
,
DSClassifier()
,
DSOutlier()
,
DSRegressor()
,
DST_SlidingWindow()
,
DST_WriteStream()
,
DST()
,
evaluate
,
predict()
,
stream_pipeline
,
update()
Other DSC:
DSC_Macro()
,
DSC_Micro()
,
DSC_R()
,
DSC_SlidingWindow()
,
DSC_Static()
,
DSC_TwoStage()
,
animate_cluster()
,
evaluate.DSC
,
get_assignment()
,
plot.DSC()
,
predict()
,
prune_clusters()
,
read_saveDSC
,
recluster()
DSC() set.seed(1000) stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05) dstream <- DSC_DStream(gridsize = .1, gaptime = 100) update(dstream, stream, 500) dstream # get micro-cluster centers get_centers(dstream) # get the micro-cluster weights get_weights(dstream) # get the number of clusters nclusters(dstream) # get the whole model as a data.frame get_model(dstream) # D-Stream also has macro-clusters get_weights(dstream, type = "macro") get_centers(dstream, type = "macro") # plot the clustering result plot(dstream, stream) plot(dstream, stream, type = "both") # predict macro clusters for new points (see predict()) points <- get_points(stream, n = 5) points predict(dstream, points, type = "macro")
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