find_causal_rules

Functions to mine causal association rules from a formal context.

Provides tools to perform fuzzy formal concept analysis, presented in Wille (1982) <doi:10.1007/978-3-642-01815-2_23> and in Ganter and Obiedkov (2016) <doi:10.1007/978-3-662-49291-8>. It provides functions to load and save a formal context, extract its concept lattice and implications. In addition, one can use the implications to compute semantic closures of fuzzy sets and, thus, build recommendation systems. Matrix factorization is provided by the GreConD+ algorithm (Belohlavek and Trneckova, 2024 <doi:10.1109/TFUZZ.2023.3330760>).

Domingo Lopez Rodriguez

fcaR

Formal Concept Analysis

Angel Mora

Jesus Dominguez

Ana Villalon

find_causal_rules function

<dl><dt>fc</dt>
<dd>A FormalContext object.</dd>
<dt>response_var</dt>
<dd>(character) The name of the response variable.</dd>
<dt>min_support</dt>
<dd>(numeric) Minimum support.</dd>
<dt>confidence_level</dt>
<dd>(numeric) Confidence level for the causality test.</dd>
<dt>max_length</dt>
<dd>(integer) Maximum length of the premise.</dd>
<dt>verbose</dt>
<dd>(logical) Show verbose output?</dd></dl>

Arguments

Causal Association Rules — find_causal_rules

<dl>

<dt>fc</dt>
<dd>A FormalContext object.</dd>


<dt>response_var</dt>
<dd>(character) The name of the response variable.</dd>


<dt>min_support</dt>
<dd>(numeric) Minimum support.</dd>


<dt>confidence_level</dt>
<dd>(numeric) Confidence level for the causality test.</dd>


<dt>max_length</dt>
<dd>(integer) Maximum length of the premise.</dd>


<dt>verbose</dt>
<dd>(logical) Show verbose output?</dd>

</dl>

find_causal_rules: Causal Association Rules

Description

Usage

Value

Arguments