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Proceedings Paper

K-nearest neighbors clustering algorithm
Author(s): Dariusz Gauza; Anna Żukowska; Robert Nowak
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Paper Abstract

Cluster analysis, understood as unattended method of assigning objects to groups solely on the basis of their measured characteristics, is the common method to analyze DNA microarray data. Our proposal is to classify the results of one nearest neighbors algorithm (1NN). The presented method well cope with complex, multidimensional data, where the number of groups is properly identified. The numerical experiments on benchmark microarray data shows that presented algorithm give a better results than k-means clustering.

Paper Details

Date Published: 25 November 2014
PDF: 6 pages
Proc. SPIE 9290, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2014, 92901I (25 November 2014); doi: 10.1117/12.2074124
Show Author Affiliations
Dariusz Gauza, Warsaw Univ. of Technology (Poland)
Anna Żukowska, Warsaw Univ. of Technology (Poland)
Robert Nowak, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 9290:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2014
Ryszard S. Romaniuk, Editor(s)

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