Tuning Larger Membership Grades for Fuzzy Association Rules

Stephen G. Matthews

doi:10.1109/FUZZ-IEEE.2014.6891765

Tuning Larger Membership Grades for Fuzzy Association Rules

Stephen G. Matthews

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

Sigma count measures scalar cardinality of fuzzy sets. A problem with sigma count is that values of scalar cardinality are calculated entirely from many small membership grades or entirely from few large membership grades. Two novel scalar cardinality measures are proposed for the fitness of a genetic algorithm for tuning membership functions prior to fuzzy association rule mining so that individual membership grades are larger. Preliminary results show a decrease in small membership grades and an increase in large membership grades for fuzzy association rules tested on real-world benchmark datasets.

Original language	English
Title of host publication	IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Pages	1960
Number of pages	1967
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2014.6891765
Publication status	Published - 2014

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AB - Sigma count measures scalar cardinality of fuzzy sets. A problem with sigma count is that values of scalar cardinality are calculated entirely from many small membership grades or entirely from few large membership grades. Two novel scalar cardinality measures are proposed for the fitness of a genetic algorithm for tuning membership functions prior to fuzzy association rule mining so that individual membership grades are larger. Preliminary results show a decrease in small membership grades and an increase in large membership grades for fuzzy association rules tested on real-world benchmark datasets.

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Tuning Larger Membership Grades for Fuzzy Association Rules

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