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Statements

Subject Item
n2:RIV%2F61988987%3A17610%2F14%3AA1501B7C%21RIV15-MSM-17610___
rdf:type
skos:Concept n6:Vysledek
dcterms:description
Fuzzy association analysis extracts relationships from data. The result of fuzzy association analysis depends on a chosen t-norm that is used for calculating confidence and support measures of mined association rules. We show that the set of mined association rules might change depending on the t-norm. We measure the distances of sets of mined rules with different t-norms and also with set of rules mined by crisp association analysis. We experiment with various datasets and partitioning methods to examine relationships of mined rules by different t-norms. Our experiments shed new light on application of fuzzy association mining and confirm that fuzzy association analysis usually brings signifficantly different results when compared to results given by crisp (non-fuzzy) association analysis. Fuzzy association analysis extracts relationships from data. The result of fuzzy association analysis depends on a chosen t-norm that is used for calculating confidence and support measures of mined association rules. We show that the set of mined association rules might change depending on the t-norm. We measure the distances of sets of mined rules with different t-norms and also with set of rules mined by crisp association analysis. We experiment with various datasets and partitioning methods to examine relationships of mined rules by different t-norms. Our experiments shed new light on application of fuzzy association mining and confirm that fuzzy association analysis usually brings signifficantly different results when compared to results given by crisp (non-fuzzy) association analysis.
dcterms:title
The Role of a T-norm and Partitioning in Fuzzy Association Analysis The Role of a T-norm and Partitioning in Fuzzy Association Analysis
skos:prefLabel
The Role of a T-norm and Partitioning in Fuzzy Association Analysis The Role of a T-norm and Partitioning in Fuzzy Association Analysis
skos:notation
RIV/61988987:17610/14:A1501B7C!RIV15-MSM-17610___
n3:aktivita
n12:S n12:P
n3:aktivity
P(ED1.1.00/02.0070), S
n3:dodaniDat
n8:2015
n3:domaciTvurceVysledku
n14:9782745 n14:4033507
n3:druhVysledku
n18:D
n3:duvernostUdaju
n5:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
43074
n3:idVysledku
RIV/61988987:17610/14:A1501B7C
n3:jazykVysledku
n15:eng
n3:klicovaSlova
fuzzy association analysis; t-norm; association rules
n3:klicoveSlovo
n16:association%20rules n16:t-norm n16:fuzzy%20association%20analysis
n3:kontrolniKodProRIV
[526C627435F8]
n3:mistoKonaniAkce
Varšava
n3:nazevZdroje
Strengthening Links between Data Analysis and Soft Computing
n3:obor
n21:BA
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n10:ED1.1.00%2F02.0070
n3:rokUplatneniVysledku
n8:2014
n3:tvurceVysledku
Rusnok, Pavel Kupka, Jiří
n3:typAkce
n17:WRD
n3:zahajeniAkce
2014-09-22+02:00
s:numberOfPages
9
n13:hasPublisher
Springer-Verlag
n11:isbn
978-3-319-10764-6
n20:organizacniJednotka
17610