A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics

The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original k -means clustering technique—the Fast, Efficient, and Scalable k...

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Veröffentlicht in:	EURASIP journal on bioinformatics & systems biology Jg. 2010; H. 1; S. 1 - 14
1. Verfasser:	Oyana, Tonny J
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 2010 Springer Nature B.V Springer
Schlagworte:	Algorithms Bioinformatics Biology Biomedical Engineering and Bioengineering Blood Blood levels Cluster analysis Computational Biology/Bioinformatics Data mining Elevated Engineering Field study Gene expression Human subjects Mathematical analysis Mean square errors Methods Quality Research Article Signal,Image and Speech Processing Systems Biology Visual Cluster Center Housing Unit Synthetic Dataset Neighbor Query Mean Square Error
ISSN:	1687-4145, 1687-4153, 1687-4153
Online-Zugang:	Volltext
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