An Alternative Approach for Binary and Categorical Self-Organizing Maps

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dc.contributor.authorSantana, Alessandra [UNIFESP]
dc.contributor.authorMorais, Alessandra [UNIFESP]
dc.contributor.authorQuiles, Marcos G. [UNIFESP]
dc.coverageNew York
dc.date.accessioned2020-07-17T14:03:18Z
dc.date.available2020-07-17T14:03:18Z
dc.date.issued2017
dc.description.abstractOne of the most used neural network model for clustering data is the Self-Organizing Map (SOM). Over the years, it has been applied in many areas, from computing to biology, and therefore a wide range of data types have been considered. Originally, the SOM was developed to take real-valued data into account. Thus, learning other data types, such as binary and category data, remains a challenge. This work proposes an alternative and effective modified SOM, to better cluster binary and categorical data.en
dc.description.affiliationUniv Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, Brazil
dc.description.affiliationUnifespUniv Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, Brazil
dc.description.sourceWeb of Science
dc.format.extent2604-2610
dc.identifierhttps://doi.org/10.1109/IJCNN.2017.7966174
dc.identifier.citation2017 International Joint Conference On Neural Networks (Ijcnn). New York, v. , p. 2604-2610, 2017.
dc.identifier.doi10.1109/IJCNN.2017.7966174
dc.identifier.issn2161-4393
dc.identifier.urihttps://repositorio.unifesp.br/handle/11600/55299
dc.identifier.wosWOS:000426968702112
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartof2017 International Joint Conference on Neural Networks (IJCNN)
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectSelf-organizing mapsen
dc.subjectbinary dataen
dc.subjectcategorical dataen
dc.subjectclusteringen
dc.subjectneural networksen
dc.titleAn Alternative Approach for Binary and Categorical Self-Organizing Mapsen
dc.typeinfo:eu-repo/semantics/conferenceObject
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