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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. (2015). A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering. Fuzzy Sets and Systems. 270, 90-110
Exportar Referência (IEEE)
A. K. Suleman,  "A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering", in Fuzzy Sets and Systems, vol. 270, pp. 90-110, 2015
Exportar BibTeX
@article{suleman2015_1711693375030,
	author = "Suleman, A.",
	title = "A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering",
	journal = "Fuzzy Sets and Systems",
	year = "2015",
	volume = "270",
	number = "",
	doi = "10.1016/j.fss.2014.07.021",
	pages = "90-110",
	url = "http://www.sciencedirect.com/science/article/pii/S016501141400342X"
}
Exportar RIS
TY  - JOUR
TI  - A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering
T2  - Fuzzy Sets and Systems
VL  - 270
AU  - Suleman, A.
PY  - 2015
SP  - 90-110
SN  - 0165-0114
DO  - 10.1016/j.fss.2014.07.021
UR  - http://www.sciencedirect.com/science/article/pii/S016501141400342X
AB  - We propose an alternative approach to fuzzy c-means clustering which eliminates the weighting exponent parameter of conventional algorithms. It is based on a particular convex factorisation of data matrix. The proposed method is invariant under certain linear transformations of the data including principal component analysis. We tested its accuracy using both synthetic data and real datasets, and compared it to that provided by the usual fuzzy c-means algorithm. We were able to ascertain that our proposal can be a credible yet easier alternative to this approach to fuzzy clustering. Moreover, it showed no noticeable sensitivity to the initial guess of the partition matrix.
ER  -