DST: Conceptual Base Class for All Data Stream Mining Tasks

In stream: Infrastructure for Data Stream Mining

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(), DSClassifier(), DSC(), DSOutlier(), DSRegressor(), DST_SlidingWindow(), DST_WriteStream(), evaluate, predict(), stream_pipeline, update()

Examples

DST()

stream documentation built on March 7, 2023, 6:09 p.m.