applyCuts

Applies cut points to input data frame.

Implements the Classification-based on
Association Rules (CBA) algorithm for association rule classification.
The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>,
contains several convenience methods that allow to automatically
set CBA parameters (minimum confidence, minimum support) and it also natively
handles numeric attributes by integrating a pre-discretization step.
The rule generation phase is handled by the 'arules' package.
To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the
'qCBA' package is suggested.

Tomas Kliegr

Association Rule Classification

applyCuts function

<dl><dt>df</dt>
<dd>input data frame.</dd>
<dt>cutp</dt>
<dd>a list of vectors with cutpoints (for more information see <code>applyCut</code>).</dd>
<dt>infinite_bounds</dt>
<dd>a logical indicating how the bounds on the extremes should look like (for more information see <code>applyCut</code>)</dd>
<dt>labels</dt>
<dd>a logical indicating whether the bins of the discretized data should be represented by integer codes or as interval notation using (a;b] when set to TRUE.</dd></dl>

Arguments

Apply Cut Points to Data Frame — applyCuts

<dl>

<dt>df</dt>
<dd>input data frame.</dd>


<dt>cutp</dt>
<dd>a list of vectors with cutpoints (for more information see <code>applyCut</code>).</dd>


<dt>infinite_bounds</dt>
<dd>a logical indicating how the bounds on the extremes should look like (for more information see <code>applyCut</code>)</dd>


<dt>labels</dt>
<dd>a logical indicating whether the bins of the discretized data should be represented by integer codes or as interval notation using (a;b] when set to TRUE.</dd>

</dl>

applyCuts: Apply Cut Points to Data Frame

Description

Usage

Value

Arguments

See Also

Examples