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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)
Suleman, A. (2017). Validation of archetypal analysis. In 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017. Naples: IEEE.
Exportar Referência (IEEE)
A. K. Suleman,  "Validation of archetypal analysis", in 2017 IEEE Int. Conf. on Fuzzy Systems, FUZZ 2017, Naples, IEEE, 2017
Exportar BibTeX
@inproceedings{suleman2017_1714035474291,
	author = "Suleman, A.",
	title = "Validation of archetypal analysis",
	booktitle = "2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017",
	year = "2017",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/FUZZ-IEEE.2017.8015385",
	publisher = "IEEE",
	address = "Naples",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/8015385/"
}
Exportar RIS
TY  - CPAPER
TI  - Validation of archetypal analysis
T2  - 2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
AU  - Suleman, A.
PY  - 2017
SN  - 1558-4739
DO  - 10.1109/FUZZ-IEEE.2017.8015385
CY  - Naples
UR  - https://ieeexplore.ieee.org/document/8015385/
AB  - We use an information-theoretic criterion to assess the goodness-of-fit of the output of archetypal analysis (AA), also intended as a fuzzy clustering tool. It is an adaptation of an existing AIC-like measure to the specifics of AA. We test its effectiveness using artificial data and some data sets arising from real life problems. In most cases, the results achieved are similar to those provided by an external similarity index. The average reconstruction accuracy is about 93%.
ER  -