EventsMOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed.
Event submissions
Published
with-doi10.3390/MOL2NET-1-e010
This submission belongs to the session 03. USEDAT-01: USA-Europe Data Analysis Training Congress, Cambridge, UK-Bilbao, Spain-Miami, USA, 2015 of the event MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed.
Published date
04 Dec, 2015
Citation
Isis Bonet, Andrea Mesa-Múnera, Adriana Escobar, Juan Fernando Alzate, Iterative Kernel K-means for Metagenomic Sequences, in Proceedings of MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed., 5 December–15 December 2015, MDPI: Basel, Switzerland, doi: 10.3390/MOL2NET-1-e010
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Iterative Kernel K-means for Metagenomic Sequences

Isis Bonet 1
Andrea Mesa-Múnera 1
Adriana Escobar 1
Juan Fernando Alzate 2
1. Escuela de Ingeniería de Antioquia
2. Centro Nacional de Secuenciación Genómica, Facultad de Medicina, Universidad de Antioquia
Abstract

This paper shows an iterative clustering method based on kernel k-means, which changes the parameter k automatically in each iteration of the algorithm. In addition, a way to initialize the centroids is proposed. The method is applied to a binning process in metagenomics using a complex database with different organisms. The aim of this method is to reduce the sensitivity of clusters based on strength measures. The results demonstrate that the proposed method is better than the simple kernel k-means for metagenome databases.

Keywords
Metagenomics
k-means
clustering
bioinformatics
Manuscript
6-MOL2NET_Metagenomic_2.pdf
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