| DST {stream} | R Documentation |
Conceptual Base Class for All Data Stream Mining Tasks
Description
Conceptual base class for all data stream mining tasks.
Usage
DST(...)
description(x, ...)
get_model(x, ...)
Arguments
... |
Further arguments. |
x |
an object of a concrete implementation of a DST. |
Details
Base class for data stream mining tasks. Types of DST are
-
DSAggregate to aggregate data streams (e.g., with a sliding window).
-
DSC for data stream clustering.
-
DSClassifier classification for data streams.
-
DSRegressor regression for data streams.
-
DSOutlier outlier detection for data streams.
-
DSFP frequent pattern mining for data streams.
The common interface for all DST classes consists of
-
update()update the DST with data points. description() a string describing the DST.
get_model() returns the DST's current model (often as a data.frame or a R model object).
-
predict()use the learned DST model to make predictions.
and the methods in the Methods Section below.
Author(s)
Michael Hahsler
See Also
Other DST:
DSAggregate(),
DSC(),
DSClassifier(),
DSOutlier(),
DSRegressor(),
DST_SlidingWindow(),
DST_WriteStream(),
evaluate,
predict(),
stream_pipeline,
update()
Examples
DST()