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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. (2021). Comparing different approaches to archetypal analysis as a fuzzy clustering tool. International Journal of Fuzzy Systems. 23 (7), 2182-2199
Exportar Referência (IEEE) 
A. K. Suleman,  "Comparing different approaches to archetypal analysis as a fuzzy clustering tool", in Int. Journal of Fuzzy Systems, vol. 23, no. 7, pp. 2182-2199, 2021
Exportar BibTeX 
@article{suleman2021_1715539595794,
	author = "Suleman, A.",
	title = "Comparing different approaches to archetypal analysis as a fuzzy clustering tool",
	journal = "International Journal of Fuzzy Systems",
	year = "2021",
	volume = "23",
	number = "7",
	doi = "10.1007/s40815-021-01088-9",
	pages = "2182-2199",
	url = "https://www.springer.com/journal/40815"
}
Exportar RIS 
TY  - JOUR
TI  - Comparing different approaches to archetypal analysis as a fuzzy clustering tool
T2  - International Journal of Fuzzy Systems
VL  - 23
IS  - 7
AU  - Suleman, A.
PY  - 2021
SP  - 2182-2199
SN  - 1562-2479
DO  - 10.1007/s40815-021-01088-9
UR  - https://www.springer.com/journal/40815
AB  - We summarize the results of an intensive simulation study carried out to compare the performance of three approaches to archetypal analysis regarded as a fuzzy clustering tool: the original approach, namely that of Cutler and Breiman (Technometrics 36(4):338–347, 1994), the Ding et al. (IEEE Trans Pattern Anal Mach Intell 32(1):45–55, 2010) proposal, and the factorized fuzzy c-means algorithm. The artificial data we use in our experiment are generated from polytopes in low-dimensional Rn spaces (2 ≤ n≤ 7) , and comprise a diversity of cluster contexts. The simulation results show that the original proposal is generally a more accurate method to uncover the cluster structure hidden in the data and to reproduce the data themselves. However, this supremacy, if any, is not clear for the data generated from real life problems, and devoted to unsupervised clustering problems.
ER  -