Detection of Fuzzy Association Rules by Fuzzy Transforms
Keyword(s):
Rule Set
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We present a new method based on the use of fuzzy transforms for detecting coarse-grained association rules in the datasets. The fuzzy association rules are represented in the form of linguistic expressions and we introduce a pre-processing phase to determine the optimal fuzzy partition of the domains of the quantitative attributes. In the extraction of the fuzzy association rules we use the AprioriGen algorithm and a confidence index calculated via the inverse fuzzy transform. Our method is applied to datasets of the 2001 census database of the district of Naples (Italy); the results show that the extracted fuzzy association rules provide a correct coarse-grained view of the data association rule set.
2010 ◽
Vol 108-111
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pp. 50-56
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2003 ◽
Vol 16
(3)
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pp. 137-147
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2014 ◽
Vol 8
(1)
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pp. 303-307
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2021 ◽
Vol 23
(2)
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pp. 1137
2013 ◽
Vol 9
(1)
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pp. 1-27
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2012 ◽
Vol 241-244
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pp. 1589-1592
2010 ◽
pp. 47-64
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Keyword(s):
2014 ◽
Vol 687-691
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pp. 1337-1341