Clustering based on the geodesic distance on Gaussian manifolds for the automatic classification of disruptions

by A. Murari, P. Boutot, J. Vega, M. Gelfusa, R. Moreno, G. Verdoolaege, P. C. de Vries

Reference:

Clustering based on the geodesic distance on Gaussian manifolds for the automatic classification of disruptions (A. Murari, P. Boutot, J. Vega, M. Gelfusa, R. Moreno, G. Verdoolaege, P. C. de Vries), In NUCLEAR FUSION, volume 53, 2013.

Bibtex Entry:

@article{nr21,
	Author = {Murari, A. and Boutot, P. and Vega, J. and Gelfusa, M. and Moreno, R. and Verdoolaege, G. and de Vries, P. C.},
	Date = {MAR 2013},
	Date-Added = {2013-10-12 14:28:31 +0000},
	Date-Modified = {2013-10-12 14:28:31 +0000},
	Doi = {10.1088/0029-5515/53/3/033006},
	Group-Authors = {JET-EFDA Contributors},
	Isi = {WOS:000315417000006},
	Issn = {0029-5515},
	Journal = {NUCLEAR FUSION},
	Month = {Mar},
	Number = {3},
	OI = {Verdoolaege, Geert/0000-0002-2640-4527},
	Pages = {033006},
	Publication-Type = {J},
	RI = {Verdoolaege, Geert/I-4655-2012},
	Times-Cited = {1},
	Title = {Clustering based on the geodesic distance on Gaussian manifolds for the automatic classification of disruptions},
	Volume = {53},
	Year = {2013},
	Z8 = {0},
	Z9 = {1},
	ZB = {0},
	ZS = {0},
	Bdsk-Url-1 = {http://dx.doi.org/10.1088/0029-5515/53/3/033006}}

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Clustering based on the geodesic distance on Gaussian manifolds for the automatic classification of disruptions