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Statements

Subject Item
n2:RIV%2F61989592%3A15310%2F05%3A00002478%21RIV06-AV0-15310___
rdf:type
skos:Concept n14:Vysledek
dcterms:description
Formalní konceptuální analýza s uživatelem vybranou úrovní granularity atributů. Formal concept analysis (FCA) is a method of exploratory analysis of object-attribute data tables. The two main outputs are a hierarchical structure of clusters (so-called formal concepts) and a non-redundant basis of so-called attribute implications. An important topic in FCA is to cope with a possibly large number of resulting clusters. We propose a method to control the number of clusters by means of specification of a granularity level of attributes. A user selects an appropriate level of granularity of each attribute. If the corresponding set of clusters is too large, the user can select a lower level of granularity for appropriate attributes. The resulting set of clusters is then smaller and can be seen as a rougher version of the original set of clusters. If the corresponding set of clusters is too small, the user can select a finer level of granularity for appropriate attributes. The resulting set of clusters is then larger and can be seen as a refinement of the original set of clusters. The p Formal concept analysis (FCA) is a method of exploratory analysis of object-attribute data tables. The two main outputs are a hierarchical structure of clusters (so-called formal concepts) and a non-redundant basis of so-called attribute implications. An important topic in FCA is to cope with a possibly large number of resulting clusters. We propose a method to control the number of clusters by means of specification of a granularity level of attributes. A user selects an appropriate level of granularity of each attribute. If the corresponding set of clusters is too large, the user can select a lower level of granularity for appropriate attributes. The resulting set of clusters is then smaller and can be seen as a rougher version of the original set of clusters. If the corresponding set of clusters is too small, the user can select a finer level of granularity for appropriate attributes. The resulting set of clusters is then larger and can be seen as a refinement of the original set of clusters. The p
dcterms:title
Formal concept analysis over attributes with levels of granularity Formal concept analysis over attributes with levels of granularity Formalní konceptuální analýza nad atributy s úrovněmi granularity
skos:prefLabel
Formal concept analysis over attributes with levels of granularity Formal concept analysis over attributes with levels of granularity Formalní konceptuální analýza nad atributy s úrovněmi granularity
skos:notation
RIV/61989592:15310/05:00002478!RIV06-AV0-15310___
n3:strany
728-736
n3:aktivita
n18:P
n3:aktivity
P(1ET101370417)
n3:dodaniDat
n4:2006
n3:domaciTvurceVysledku
n11:2764741 n11:9623264
n3:druhVysledku
n12:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
521930
n3:idVysledku
RIV/61989592:15310/05:00002478
n3:jazykVysledku
n15:eng
n3:klicovaSlova
Formal concept analysis; levels of granularity
n3:klicoveSlovo
n10:Formal%20concept%20analysis n10:levels%20of%20granularity
n3:kontrolniKodProRIV
[20DD80E0A057]
n3:mistoKonaniAkce
Wien
n3:mistoVydani
New York
n3:nazevZdroje
International Conference on Computational Intelligence for Modelling Control and Automation - CIMCA'2005
n3:obor
n21:BD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n6:1ET101370417
n3:rokUplatneniVysledku
n4:2005
n3:tvurceVysledku
Bělohlávek, Radim Sklenář, Vladimír
n3:typAkce
n17:WRD
n3:zahajeniAkce
2005-01-01+01:00
s:numberOfPages
9
n19:hasPublisher
IEEE Computer Society Press
n20:isbn
0-7695-2504-0
n5:organizacniJednotka
15310