- Clustering Variables by Classical Approaches and Neural Network Boole…
Počet záznamů: 1  

Clustering Variables by Classical Approaches and Neural Network Boolean Factor Analysis

  1. 1.
    SYSNO ASEP0314040
    Document TypeC - Proceedings Paper (int. conf.)
    R&D Document TypeConference Paper
    TitleClustering Variables by Classical Approaches and Neural Network Boolean Factor Analysis
    TitleShlukování proměnných klasickými metodami a pomocí neurosíťové Booleovské faktorové analýzy
    Author(s) Frolov, A. A. (RU)
    Húsek, Dušan (UIVT-O) RID, SAI, ORCID
    Řezanková, H. (CZ)
    Snášel, V. (CZ)
    Polyakov, P.Y. (RU)
    Source TitleInternational Joint Conference on Neural Networks. - Piscataway : IEEE, 2008 - ISBN 978-1-4244-1820-6
    Pagess. 3742-3746
    Number of pages5 s.
    ActionIJCNN 2008. International Joint Conference on Neural Networks
    Event date01.06.2008-08.06.2008
    VEvent locationHong Kong
    CountryCN - China
    Event typeWRD
    Languageeng - English
    CountryUS - United States
    Keywordsclustering ; Boolean factor analysis ; linear factor analysis ; overlapping classes
    Subject RIVBB - Applied Statistics, Operational Research
    R&D Projects1M0567 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
    1ET100300414 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR)
    CEZAV0Z10300504 - UIVT-O (2005-2011)
    UT WOS000263827202095
    EID SCOPUS56349087933
    DOI https://doi.org/10.1109/IJCNN.2008.4634335
    AnnotationIn this paper, we compare three methods for grouping of binary variables: neural network Boolean factor analysis , hierarchical clustering, and a linear factor analysis on the mushroom dataset . In contrast to the latter two traditional methods, the advantage of neural network Boolean factor analysis is its ability to reveal overlapping classes in the dataset. It is shown that the mushroom dataset provides a good demonstration of this advantage because it contains both disjunctive and overlapping classes.
    WorkplaceInstitute of Computer Science
    ContactTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Year of Publishing2009
Počet záznamů: 1  

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