K-nearest neighbors clustering algorithm

Dariusz Gauza; Anna Żukowska; Robert Nowak

doi:10.1117/12.2074124

25 November 2014 K-nearest neighbors clustering algorithm

Dariusz Gauza, Anna Żukowska, Robert Nowak

Proceedings Volume 9290, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2014; 92901I (2014) https://doi.org/10.1117/12.2074124
Event: Symposium on Photonics Applications in Astronomy, Communications, Industry and High-Energy Physics Experiments, 2014, Warsaw, Poland

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.

Citation Download Citation

Dariusz Gauza, Anna Żukowska, and Robert Nowak "K-nearest neighbors clustering algorithm", Proc. SPIE 9290, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2014, 92901I (25 November 2014); https://doi.org/10.1117/12.2074124

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