Quality measures for fuzzy predicates in conjunctive and disjunctive normal forms

Association rule mining is a very popular data mining technique. Rules in this technique are often used to identify and represent de-pendencies between attributes in databases. Specifically, fuzzy association rules are rules that use the concepts of fuzzy sets and can be considered as a special case...

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Autores:: Ceruto Cordovés, Taymi
Lapeira Mena, Orenia
Rosete Suarez, Alejandro

Tipo de recurso:: Article of journal

Fecha de publicación:: 2014

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

Description
Summary:	Association rule mining is a very popular data mining technique. Rules in this technique are often used to identify and represent de-pendencies between attributes in databases. Specifically, fuzzy association rules are rules that use the concepts of fuzzy sets and can be considered as a special case of fuzzy predicates. Many quality measures have been defined for fuzzy association rules, but all consider a specific structure: antecedent and consequence. In the case of fuzzy predicates in the normal form (i.e., conjunctive or disjunctive), it is necessary to define different quality measures that do not consider the structure as an antecedent or a consequence. The only available measure for this scenario is the fuzzy predicate truth value (FPTV), which has serious limitations. The evaluation of fuzzy predicates in the normal form through appropriate quality measures has not yet been clearly defined in the literature. Thus, we propose several quality measures specifically for fuzzy predicates in the conjunctive (CNF) and disjunctive (DNF) normal forms. Experi-mental studies illustrate the use of the proposed measures and allow some general conclusions about each measure.

Quality measures for fuzzy predicates in conjunctive and disjunctive normal forms

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