Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Silvestre, C. , Cardoso, M. & Figueiredo, M. (2013). Clustering and selecting categorical features. In Correia, L., Reis, L. P., and Cascalho, J.  (Ed.), Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science. (pp. 331-342). Angra do Heroísmo: Springer.
Exportar Referência (IEEE) 
S. C. et al.,  "Clustering and selecting categorical features", in Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science, Correia, L., Reis, L. P., and Cascalho, J. , Ed., Angra do Heroísmo, Springer, 2013, vol. 8154, pp. 331-342
Exportar BibTeX 
@inproceedings{c.2013_1732407067729,
	author = "Silvestre, C.  and Cardoso, M. and Figueiredo, M.",
	title = "Clustering and selecting categorical features",
	booktitle = "Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science",
	year = "2013",
	editor = "Correia, L., Reis, L. P., and Cascalho, J. ",
	volume = "8154",
	number = "",
	series = "",
	doi = "10.1007/978-3-642-40669-0_29",
	pages = "331-342",
	publisher = "Springer",
	address = "Angra do Heroísmo",
	organization = "",
	url = "https://link.springer.com/book/10.1007/978-3-642-40669-0"
}
Exportar RIS 
TY  - CPAPER
TI  - Clustering and selecting categorical features
T2  - Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science
VL  - 8154
AU  - Silvestre, C. 
AU  - Cardoso, M.
AU  - Figueiredo, M.
PY  - 2013
SP  - 331-342
SN  - 0302-9743
DO  - 10.1007/978-3-642-40669-0_29
CY  - Angra do Heroísmo
UR  - https://link.springer.com/book/10.1007/978-3-642-40669-0
AB  - 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.
ER  -