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Nonparametric clustering approach towards big data


Lorimer, T; Held, J; Albert, C; Stoop, R (2016). Nonparametric clustering approach towards big data. In: NOLTA 2016: International Symposium on Nonlinear Theory and Its Applications, Yugawara, 27 November 2016 - 30 November 2016.

Abstract

Clustering in bioinformatics is a fundamental process involving computational issues that are far from being resolved. In our work, we propose a new approach to this problem and show preliminary comparisons to current leading methods in the field.

Abstract

Clustering in bioinformatics is a fundamental process involving computational issues that are far from being resolved. In our work, we propose a new approach to this problem and show preliminary comparisons to current leading methods in the field.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:30 November 2016
Deposited On:23 Feb 2018 10:15
Last Modified:13 Apr 2018 11:37
Publisher:Proceedings of the 2016 International Symposium on Nonlinear Theory and its Applications (NOLTA)
Series Name:Proceedings of NOLTA 2016
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://www.ieice.org/nolta/symposium/archive/2016/articles/1164.pdf

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