Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/27774
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Silvestre, C. | - |
dc.contributor.author | Cardoso, M. | - |
dc.contributor.author | Figueiredo, M. | - |
dc.contributor.editor | Correia, L., Reis, L. P., and Cascalho, J. | - |
dc.date.accessioned | 2023-02-07T13:21:35Z | - |
dc.date.available | 2023-02-07T13:21:35Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | Silvestre, C., Cardoso, M., & Figueiredo, M. (2013). Clustering and selecting categorical features. In L. Correia, L. P. Reis, & J. Cascalho (Eds.) Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science (vol. 8154, pp. 331-342). Springer. https://doi.org/10.1007/978-3-642-40669-0_29 | - |
dc.identifier.isbn | 978-3-642-40669-0 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10071/27774 | - |
dc.description.abstract | In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness. | eng |
dc.language.iso | eng | - |
dc.publisher | Springer | - |
dc.relation.ispartof | Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science | - |
dc.rights | openAccess | - |
dc.subject | Cluster analysis | eng |
dc.subject | Finite mixtures models | eng |
dc.subject | EM algorithm | eng |
dc.subject | Feature selection | eng |
dc.subject | Categorical variables | eng |
dc.title | Clustering and selecting categorical features | eng |
dc.type | conferenceObject | - |
dc.event.title | 16th Portuguese Conference on Artificial Intelligence, EPIA 2013 | - |
dc.event.type | Conferência | pt |
dc.event.location | Angra do Heroísmo | eng |
dc.event.date | 2013 | - |
dc.pagination | 331 - 342 | - |
dc.peerreviewed | yes | - |
dc.volume | 8154 | - |
dc.date.updated | 2023-02-07T13:19:49Z | - |
dc.description.version | info:eu-repo/semantics/acceptedVersion | - |
dc.identifier.doi | 10.1007/978-3-642-40669-0_29 | - |
iscte.identifier.ciencia | https://ciencia.iscte-iul.pt/id/ci-pub-75041 | - |
Aparece nas coleções: | BRU-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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conferenceobject_75041.pdf | 198,87 kB | Adobe PDF | Ver/Abrir |
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