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Clustering Variables by Classical Approaches and Neural Network Boolean Factor Analysis
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SYSNO ASEP 0314040 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Clustering Variables by Classical Approaches and Neural Network Boolean Factor Analysis Title Shlukování 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 Title International Joint Conference on Neural Networks. - Piscataway : IEEE, 2008 - ISBN 978-1-4244-1820-6 Pages s. 3742-3746 Number of pages 5 s. Action IJCNN 2008. International Joint Conference on Neural Networks Event date 01.06.2008-08.06.2008 VEvent location Hong Kong Country CN - China Event type WRD Language eng - English Country US - United States Keywords clustering ; Boolean factor analysis ; linear factor analysis ; overlapping classes Subject RIV BB - Applied Statistics, Operational Research R&D Projects 1M0567 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) 1ET100300414 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000263827202095 EID SCOPUS 56349087933 DOI https://doi.org/10.1109/IJCNN.2008.4634335 Annotation In 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2009
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