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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). On ill-conceived initialization in archetypal analysis. Advances in Data Analysis and Classification. 11 (4), 785-808
Exportar Referência (IEEE)
A. K. Suleman,  "On ill-conceived initialization in archetypal analysis", in Advances in Data Analysis and Classification, vol. 11, no. 4, pp. 785-808, 2017
Exportar BibTeX
@article{suleman2017_1711705945721,
	author = "Suleman, A.",
	title = "On ill-conceived initialization in archetypal analysis",
	journal = "Advances in Data Analysis and Classification",
	year = "2017",
	volume = "11",
	number = "4",
	doi = "10.1007/s11634-017-0303-0",
	pages = "785-808",
	url = "https://link.springer.com/article/10.1007/s11634-017-0303-0"
}
Exportar RIS
TY  - JOUR
TI  - On ill-conceived initialization in archetypal analysis
T2  - Advances in Data Analysis and Classification
VL  - 11
IS  - 4
AU  - Suleman, A.
PY  - 2017
SP  - 785-808
SN  - 1862-5347
DO  - 10.1007/s11634-017-0303-0
UR  - https://link.springer.com/article/10.1007/s11634-017-0303-0
AB  - We show that an improper initialization of the matrix of prototypes,  V, can be misleading, and potentially gives rise to a degenerate fuzzy partition when performing fuzzy clustering by means of an archetypal analysis. Subsequently, we propose an algorithm to correct the initial guess for  V, which is grounded in two theoretical results on convex hulls. A numerical experiment carried out to assess its accuracy, and involving more than 200,000 initializations, shows a failure rate of below 0.8%.
ER  -