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Conceptual Base Class for All Data Stream Mining Tasks

Source: R/DST.R
DST.Rd

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.

See also

Other DST: DSAggregate(), DSC(), DSClassifier(), DSOutlier(), DSRegressor(), DST_SlidingWindow(), DST_WriteStream(), evaluate, predict, stream_pipeline, update

Author

Michael Hahsler

Examples

DST()
#> DST is an abstract class and cannot be instantiated!
#> Available subclasses are:
#> 	DSAggregate,
#> 	DSC,
#> 	DSClassifier,
#> 	DSD,
#> 	DSF,
#> 	DSFP,
#> 	DSOutlier,
#> 	DSRegressor