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Record type:	stať ve sborníku (D)
Home Department:	Ústav pro výzkum a aplikace fuzzy modelování (94410)
Title:	Some dependencies among attributes given by fuzzy confirmation measures
Citace	Kupka, J. a Tomanová, I. Some dependencies among attributes given by fuzzy confirmation measures. In: Proc.EUSFLAT-LFA2011. Atlantis Press, 2011. s. 498-505. ISBN 978-90-78677-00-0.

Subtitle
Publication year:	2011
Obor:	Obecná matematika
Number of pages:	8
Page from:	498
Page to:	505
Form of publication:
ISBN code:	978-90-78677-00-0
ISSN code:
Proceedings title:	Proc.EUSFLAT-LFA2011
Proceedings:	Mezinárodní
Publisher name:	Atlantis Press
Place of publishing:	Neuveden
Country of Publication:	Sborník vydaný v zahraničí
Název konference:	EUSFLAT - LFA 2011
Místo konání konference:	Aix-Les-Bains, Francie
Datum zahájení konference:
Typ akce podle státní příslušnosti účastníků:	Celosvětová akce
WoS code:
EID:

Key words in English:	Data mining, linguistic association, confirmation measures, support measure, confidence measure, association rule.
Annotation in original language:	This paper is devoted to fuzzy association analysis. Our motivation to this work arose from mining of linguistic associations, however found relations among mined associations are valid in general. Three the most commonly used support and confidence measures are considered and various relations among found and previously known associations given by confirmation measures are studied. Good understanding of such relationships is essential for creating more efficient algorithms, for subsequent work with found associations as well as for cooperation with the consumer of the data mining process.
Annotation in english language:	This paper is devoted to fuzzy association analysis. Our motivation to this work arose from mining of linguistic associations, however found relations among mined associations are valid in general. Three the most commonly used support and confidence measures are considered and various relations among found and previously known associations given by confirmation measures are studied. Good understanding of such relationships is essential for creating more efficient algorithms, for subsequent work with found associations as well as for cooperation with the consumer of the data mining process.

References

Reference

R01:	RIV/61988987:17610/11:A1201275

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